ViT_Weeds.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (4.43.3)\n",
      "Requirement already satisfied: datasets in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (2.20.0)\n",
      "Requirement already satisfied: evaluate in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (0.4.2)\n",
      "Requirement already satisfied: accelerate in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (0.33.0)\n",
      "Requirement already satisfied: pillow in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (10.4.0)\n",
      "Requirement already satisfied: torchvision in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (0.19.0)\n",
      "Requirement already satisfied: scikit-learn in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (1.5.1)\n",
      "Requirement already satisfied: tf_keras in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (2.17.0)\n",
      "Requirement already satisfied: matplotlib in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (3.9.1)\n",
      "Requirement already satisfied: filelock in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers) (3.15.4)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers) (0.24.3)\n",
      "Requirement already satisfied: numpy>=1.17 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers) (1.23.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers) (24.1)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers) (6.0.1)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers) (2024.7.24)\n",
      "Requirement already satisfied: requests in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers) (2.32.3)\n",
      "Requirement already satisfied: safetensors>=0.4.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers) (0.4.3)\n",
      "Requirement already satisfied: tokenizers<0.20,>=0.19 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers) (0.19.1)\n",
      "Requirement already satisfied: tqdm>=4.27 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers) (4.66.4)\n",
      "Requirement already satisfied: pyarrow>=15.0.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets) (17.0.0)\n",
      "Requirement already satisfied: pyarrow-hotfix in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets) (0.6)\n",
      "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets) (0.3.8)\n",
      "Requirement already satisfied: pandas in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets) (2.2.2)\n",
      "Requirement already satisfied: xxhash in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets) (3.4.1)\n",
      "Requirement already satisfied: multiprocess in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets) (0.70.16)\n",
      "Requirement already satisfied: fsspec<=2024.5.0,>=2023.1.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from fsspec[http]<=2024.5.0,>=2023.1.0->datasets) (2024.5.0)\n",
      "Requirement already satisfied: aiohttp in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets) (3.9.5)\n",
      "Requirement already satisfied: psutil in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from accelerate) (6.0.0)\n",
      "Requirement already satisfied: torch>=1.10.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from accelerate) (2.4.0)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (4.12.2)\n",
      "Requirement already satisfied: sympy in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (1.13.1)\n",
      "Requirement already satisfied: networkx in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
      "Requirement already satisfied: jinja2 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (3.1.4)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (9.1.0.70)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (2.20.5)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
      "Requirement already satisfied: triton==3.0.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from torch>=1.10.0->accelerate) (3.0.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate) (12.5.82)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from scikit-learn) (1.13.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from scikit-learn) (3.5.0)\n",
      "Requirement already satisfied: tensorflow<2.18,>=2.17 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tf_keras) (2.17.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from matplotlib) (1.2.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from matplotlib) (4.53.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from matplotlib) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from matplotlib) (2.9.0)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from matplotlib) (6.4.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets) (1.2.0)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets) (23.1.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets) (1.4.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets) (6.0.4)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets) (1.9.3)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets) (4.0.3)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.17.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from requests->transformers) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from requests->transformers) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from requests->transformers) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from requests->transformers) (2024.7.4)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (2.1.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers>=24.3.25 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (24.3.25)\n",
      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (0.4.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (0.2.0)\n",
      "Requirement already satisfied: h5py>=3.10.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (3.11.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (18.1.1)\n",
      "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (0.4.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (3.3.0)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (4.25.3)\n",
      "Requirement already satisfied: setuptools in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (69.5.1)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (2.1.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (1.14.1)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (1.62.2)\n",
      "Requirement already satisfied: tensorboard<2.18,>=2.17 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (2.17.0)\n",
      "Requirement already satisfied: keras>=3.2.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (3.4.1)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorflow<2.18,>=2.17->tf_keras) (0.37.1)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from pandas->datasets) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from pandas->datasets) (2024.1)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from astunparse>=1.6.0->tensorflow<2.18,>=2.17->tf_keras) (0.43.0)\n",
      "Requirement already satisfied: rich in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from keras>=3.2.0->tensorflow<2.18,>=2.17->tf_keras) (13.7.1)\n",
      "Requirement already satisfied: namex in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from keras>=3.2.0->tensorflow<2.18,>=2.17->tf_keras) (0.0.8)\n",
      "Requirement already satisfied: optree in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from keras>=3.2.0->tensorflow<2.18,>=2.17->tf_keras) (0.12.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf_keras) (3.4.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf_keras) (0.7.0)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf_keras) (3.0.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from markdown>=2.6.8->tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf_keras) (7.0.1)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from rich->keras>=3.2.0->tensorflow<2.18,>=2.17->tf_keras) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from rich->keras>=3.2.0->tensorflow<2.18,>=2.17->tf_keras) (2.18.0)\n",
      "Requirement already satisfied: mdurl~=0.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow<2.18,>=2.17->tf_keras) (0.1.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install transformers datasets evaluate accelerate pillow torchvision scikit-learn tf_keras matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9437ee0adfc46debcba6f5ce033cba9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b04a99f933d4c1fa7ecf64b9edd1446",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Resolving data files:   0%|          | 0/72003 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70582e568dcf46aca0e55eb02a197d73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Resolving data files:   0%|          | 0/8001 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf20df28201d47938925461c3486e792",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Resolving data files:   0%|          | 0/20002 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "148cf86c499c4557b4e581df4bf38817",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0/72003 [00:00<?, ?files/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9703232b00b447fcb3d7cbafe6194669",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0/8001 [00:00<?, ?files/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3634da1ce8fc400e9c986be5607c2b35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0/20002 [00:00<?, ?files/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0744382ce3644b18b40888b3222891ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6a428fce17448f9b26a8ee6a75573a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3aa8c796a6234edc93f98543377ec43b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "efbda63b9857455b831edb09154d9812",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a170670532f4949a346973a1d7e7aab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/36002 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6687fc929b64b308071f02a50d58c6f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/361 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5852e707714f45ceaa18a6b38e6f99e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/36001 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1422c68c9c2c4220b47cb91c8b2e4dfe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/361 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34ec274f661f4e67a11f2a775944a688",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a236ba1a70074128bda0e80e3f3a3a5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/8001 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ad70f883e134a5a9c4ee95e96b5e711",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/81 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc51ef1c0bad4709a4a2e28736b5fca3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d632e3e57bd456e9c7d30f3469f7622",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/20002 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f8a3b93f3ae4e59818659ae99bb3143",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/201 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/datasets/n1hal/Capstone_Weeds/commit/a9da38883d58a5a6da388e98ba520d468e2e4ba0', commit_message='Upload dataset', commit_description='', oid='a9da38883d58a5a6da388e98ba520d468e2e4ba0', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"imagefolder\", data_dir=\"Capstone_Weeds\")\n",
    "\n",
    "dataset.push_to_hub(\"n1hal/Capstone_Weeds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b30e6ea405574752aa303c7827df2e0a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/15.5k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bcdaccefcf947b69b06a5d5411ac1b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/389M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bdb99cc55594c55bb76583258b97c2b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/382M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e45c35043784564aa55b9f304073c64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/86.8M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa3bf9b2b4d1404e9235793ee0efa1fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/217M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be630634e48f41578ee0e20839a31493",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/72003 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "006b5322249d4da18e8d0b4cb82076fa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/8001 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "058b6655964d40d494be5d75ffcca4d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/20002 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "weeds = load_dataset(\"n1hal/Capstone_Weeds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weeds = weeds.train_test_split(test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 72003\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 8001\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 20002\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['image', 'label'],\n",
       "    num_rows: 72003\n",
       "})"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeds[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = weeds[\"train\"].features[\"label\"].names\n",
    "label2id, id2label = dict(), dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, label in enumerate(labels):\n",
    "    label2id[label] = str(i)\n",
    "    id2label[str(i)] = label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# id2label[str(79)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-07-30 21:36:06.787985: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-07-30 21:36:06.857481: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-07-30 21:36:06.872114: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-07-30 21:36:06.958856: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-07-30 21:36:08.081890: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "Fast image processor class <class 'transformers.models.vit.image_processing_vit_fast.ViTImageProcessorFast'> is available for this model. Using slow image processor class. To use the fast image processor class set `use_fast=True`.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoImageProcessor\n",
    "\n",
    "checkpoint = \"google/vit-base-patch16-224-in21k\"\n",
    "image_processor = AutoImageProcessor.from_pretrained(checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor\n",
    "\n",
    "normalize = Normalize(mean = image_processor.image_mean, std = image_processor.image_std)\n",
    "size = (\n",
    "    image_processor.size[\"shortest_edge\"]\n",
    "    if \"shortest_edge\" in image_processor.size\n",
    "    else (image_processor.size[\"height\"], image_processor.size[\"width\"])\n",
    ")\n",
    "\n",
    "_transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transforms(examples):\n",
    "    examples[\"pixel_values\"] = [_transforms(img.convert(\"RGB\")) for img in examples[\"image\"]]\n",
    "    del examples[\"image\"]\n",
    "    return examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "weeds = weeds.with_transform(transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 72003\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 8001\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 20002\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DefaultDataCollator\n",
    "\n",
    "data_collator = DefaultDataCollator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import evaluate\n",
    "\n",
    "accuracy = evaluate.load(\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    predictions, labels = eval_pred\n",
    "    predictions = np.argmax(predictions, axis = 1)\n",
    "    return accuracy.compute(predictions=predictions, references = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForImageClassification, TrainingArguments, Trainer\n",
    "\n",
    "model = AutoModelForImageClassification.from_pretrained(\n",
    "    checkpoint,\n",
    "    num_labels=len(labels),\n",
    "    id2label=id2label,\n",
    "    label2id=label2id,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir = \"Weeds_Classification\",\n",
    "    remove_unused_columns = False,\n",
    "    evaluation_strategy = \"epoch\",\n",
    "    save_strategy = \"epoch\",\n",
    "    learning_rate = 5e-5,\n",
    "    per_device_train_batch_size = 16,\n",
    "    gradient_accumulation_steps = 4,\n",
    "    num_train_epochs = 3,\n",
    "    warmup_ratio = 0.1,\n",
    "    logging_steps = 10,\n",
    "    load_best_model_at_end = True,\n",
    "    metric_for_best_model = \"accuracy\",\n",
    "    push_to_hub = True,\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model = model,\n",
    "    args = training_args,\n",
    "    data_collator = data_collator,\n",
    "    train_dataset = weeds[\"train\"],\n",
    "    eval_dataset = weeds[\"validation\"],\n",
    "    tokenizer = image_processor,\n",
    "    compute_metrics = compute_metrics,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99b927c95d574daa93384ab2714cd718",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3375 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 5.8294, 'grad_norm': 0.9497740864753723, 'learning_rate': 1.4792899408284024e-06, 'epoch': 0.01}\n",
      "{'loss': 5.8243, 'grad_norm': 0.9087894558906555, 'learning_rate': 2.9585798816568047e-06, 'epoch': 0.02}\n",
      "{'loss': 5.827, 'grad_norm': 0.9352946877479553, 'learning_rate': 4.437869822485207e-06, 'epoch': 0.03}\n",
      "{'loss': 5.8183, 'grad_norm': 0.9707036018371582, 'learning_rate': 5.917159763313609e-06, 'epoch': 0.04}\n",
      "{'loss': 5.8326, 'grad_norm': 0.9326181411743164, 'learning_rate': 7.396449704142013e-06, 'epoch': 0.04}\n",
      "{'loss': 5.8168, 'grad_norm': 0.9484497308731079, 'learning_rate': 8.875739644970414e-06, 'epoch': 0.05}\n",
      "{'loss': 5.8207, 'grad_norm': 0.9521200656890869, 'learning_rate': 1.0355029585798817e-05, 'epoch': 0.06}\n",
      "{'loss': 5.817, 'grad_norm': 0.8906506896018982, 'learning_rate': 1.1834319526627219e-05, 'epoch': 0.07}\n",
      "{'loss': 5.8186, 'grad_norm': 0.995785117149353, 'learning_rate': 1.3313609467455624e-05, 'epoch': 0.08}\n",
      "{'loss': 5.8132, 'grad_norm': 0.9234928488731384, 'learning_rate': 1.4792899408284025e-05, 'epoch': 0.09}\n",
      "{'loss': 5.8128, 'grad_norm': 0.9095359444618225, 'learning_rate': 1.6272189349112425e-05, 'epoch': 0.1}\n",
      "{'loss': 5.8138, 'grad_norm': 0.9514939188957214, 'learning_rate': 1.7751479289940828e-05, 'epoch': 0.11}\n",
      "{'loss': 5.8066, 'grad_norm': 0.9680359959602356, 'learning_rate': 1.923076923076923e-05, 'epoch': 0.12}\n",
      "{'loss': 5.811, 'grad_norm': 0.9012009501457214, 'learning_rate': 2.0710059171597635e-05, 'epoch': 0.12}\n",
      "{'loss': 5.8024, 'grad_norm': 0.9819561839103699, 'learning_rate': 2.2189349112426034e-05, 'epoch': 0.13}\n",
      "{'loss': 5.8035, 'grad_norm': 0.9477075338363647, 'learning_rate': 2.3668639053254438e-05, 'epoch': 0.14}\n",
      "{'loss': 5.7995, 'grad_norm': 0.9122418165206909, 'learning_rate': 2.514792899408284e-05, 'epoch': 0.15}\n",
      "{'loss': 5.796, 'grad_norm': 0.8803086876869202, 'learning_rate': 2.6627218934911247e-05, 'epoch': 0.16}\n",
      "{'loss': 5.7887, 'grad_norm': 1.0798852443695068, 'learning_rate': 2.8106508875739644e-05, 'epoch': 0.17}\n",
      "{'loss': 5.7829, 'grad_norm': 0.9175708293914795, 'learning_rate': 2.958579881656805e-05, 'epoch': 0.18}\n",
      "{'loss': 5.7806, 'grad_norm': 0.9696438908576965, 'learning_rate': 3.106508875739645e-05, 'epoch': 0.19}\n",
      "{'loss': 5.7695, 'grad_norm': 1.0633084774017334, 'learning_rate': 3.254437869822485e-05, 'epoch': 0.2}\n",
      "{'loss': 5.7616, 'grad_norm': 0.9608509540557861, 'learning_rate': 3.402366863905326e-05, 'epoch': 0.2}\n",
      "{'loss': 5.7628, 'grad_norm': 0.9794811606407166, 'learning_rate': 3.5502958579881656e-05, 'epoch': 0.21}\n",
      "{'loss': 5.7523, 'grad_norm': 0.943600594997406, 'learning_rate': 3.698224852071006e-05, 'epoch': 0.22}\n",
      "{'loss': 5.7485, 'grad_norm': 0.9633746147155762, 'learning_rate': 3.846153846153846e-05, 'epoch': 0.23}\n",
      "{'loss': 5.7395, 'grad_norm': 0.9549353718757629, 'learning_rate': 3.9940828402366866e-05, 'epoch': 0.24}\n",
      "{'loss': 5.7339, 'grad_norm': 0.9791706800460815, 'learning_rate': 4.142011834319527e-05, 'epoch': 0.25}\n",
      "{'loss': 5.7144, 'grad_norm': 0.9533138275146484, 'learning_rate': 4.289940828402367e-05, 'epoch': 0.26}\n",
      "{'loss': 5.7006, 'grad_norm': 0.9881207346916199, 'learning_rate': 4.437869822485207e-05, 'epoch': 0.27}\n",
      "{'loss': 5.6986, 'grad_norm': 0.987064778804779, 'learning_rate': 4.585798816568048e-05, 'epoch': 0.28}\n",
      "{'loss': 5.6959, 'grad_norm': 1.0180878639221191, 'learning_rate': 4.7337278106508875e-05, 'epoch': 0.28}\n",
      "{'loss': 5.6742, 'grad_norm': 0.989738941192627, 'learning_rate': 4.881656804733728e-05, 'epoch': 0.29}\n",
      "{'loss': 5.6803, 'grad_norm': 1.0162245035171509, 'learning_rate': 4.996707276918011e-05, 'epoch': 0.3}\n",
      "{'loss': 5.669, 'grad_norm': 1.0447462797164917, 'learning_rate': 4.9802436615080674e-05, 'epoch': 0.31}\n",
      "{'loss': 5.6301, 'grad_norm': 1.0307732820510864, 'learning_rate': 4.963780046098123e-05, 'epoch': 0.32}\n",
      "{'loss': 5.6523, 'grad_norm': 0.9696402549743652, 'learning_rate': 4.947316430688179e-05, 'epoch': 0.33}\n",
      "{'loss': 5.6303, 'grad_norm': 0.9930351376533508, 'learning_rate': 4.930852815278235e-05, 'epoch': 0.34}\n",
      "{'loss': 5.6219, 'grad_norm': 1.0661391019821167, 'learning_rate': 4.914389199868291e-05, 'epoch': 0.35}\n",
      "{'loss': 5.6131, 'grad_norm': 1.0188394784927368, 'learning_rate': 4.8979255844583474e-05, 'epoch': 0.36}\n",
      "{'loss': 5.5867, 'grad_norm': 0.9948827028274536, 'learning_rate': 4.881461969048403e-05, 'epoch': 0.36}\n",
      "{'loss': 5.5683, 'grad_norm': 1.0241193771362305, 'learning_rate': 4.864998353638459e-05, 'epoch': 0.37}\n",
      "{'loss': 5.5603, 'grad_norm': 1.0021259784698486, 'learning_rate': 4.848534738228515e-05, 'epoch': 0.38}\n",
      "{'loss': 5.5611, 'grad_norm': 1.0153981447219849, 'learning_rate': 4.832071122818571e-05, 'epoch': 0.39}\n",
      "{'loss': 5.5451, 'grad_norm': 1.0295703411102295, 'learning_rate': 4.815607507408627e-05, 'epoch': 0.4}\n",
      "{'loss': 5.5419, 'grad_norm': 1.0398471355438232, 'learning_rate': 4.799143891998683e-05, 'epoch': 0.41}\n",
      "{'loss': 5.5239, 'grad_norm': 1.0211623907089233, 'learning_rate': 4.7826802765887394e-05, 'epoch': 0.42}\n",
      "{'loss': 5.5251, 'grad_norm': 1.025394082069397, 'learning_rate': 4.766216661178795e-05, 'epoch': 0.43}\n",
      "{'loss': 5.5137, 'grad_norm': 1.0252705812454224, 'learning_rate': 4.749753045768851e-05, 'epoch': 0.44}\n",
      "{'loss': 5.4893, 'grad_norm': 1.1059173345565796, 'learning_rate': 4.733289430358907e-05, 'epoch': 0.44}\n",
      "{'loss': 5.4775, 'grad_norm': 1.0362131595611572, 'learning_rate': 4.716825814948963e-05, 'epoch': 0.45}\n",
      "{'loss': 5.455, 'grad_norm': 1.0863900184631348, 'learning_rate': 4.700362199539019e-05, 'epoch': 0.46}\n",
      "{'loss': 5.4574, 'grad_norm': 1.064353108406067, 'learning_rate': 4.683898584129075e-05, 'epoch': 0.47}\n",
      "{'loss': 5.4573, 'grad_norm': 1.0639770030975342, 'learning_rate': 4.667434968719131e-05, 'epoch': 0.48}\n",
      "{'loss': 5.4273, 'grad_norm': 1.0746970176696777, 'learning_rate': 4.650971353309187e-05, 'epoch': 0.49}\n",
      "{'loss': 5.4074, 'grad_norm': 1.0758410692214966, 'learning_rate': 4.634507737899243e-05, 'epoch': 0.5}\n",
      "{'loss': 5.4033, 'grad_norm': 1.0645757913589478, 'learning_rate': 4.618044122489299e-05, 'epoch': 0.51}\n",
      "{'loss': 5.3828, 'grad_norm': 1.191333293914795, 'learning_rate': 4.601580507079355e-05, 'epoch': 0.52}\n",
      "{'loss': 5.3835, 'grad_norm': 1.102504849433899, 'learning_rate': 4.585116891669411e-05, 'epoch': 0.52}\n",
      "{'loss': 5.3557, 'grad_norm': 1.171553134918213, 'learning_rate': 4.568653276259467e-05, 'epoch': 0.53}\n",
      "{'loss': 5.3869, 'grad_norm': 1.14118230342865, 'learning_rate': 4.5521896608495226e-05, 'epoch': 0.54}\n",
      "{'loss': 5.3617, 'grad_norm': 1.0469740629196167, 'learning_rate': 4.535726045439579e-05, 'epoch': 0.55}\n",
      "{'loss': 5.3716, 'grad_norm': 1.0866329669952393, 'learning_rate': 4.519262430029635e-05, 'epoch': 0.56}\n",
      "{'loss': 5.3579, 'grad_norm': 1.26162850856781, 'learning_rate': 4.502798814619691e-05, 'epoch': 0.57}\n",
      "{'loss': 5.3452, 'grad_norm': 1.2066603899002075, 'learning_rate': 4.486335199209747e-05, 'epoch': 0.58}\n",
      "{'loss': 5.338, 'grad_norm': 1.1669186353683472, 'learning_rate': 4.469871583799803e-05, 'epoch': 0.59}\n",
      "{'loss': 5.3395, 'grad_norm': 1.163892388343811, 'learning_rate': 4.453407968389859e-05, 'epoch': 0.6}\n",
      "{'loss': 5.2991, 'grad_norm': 1.1629446744918823, 'learning_rate': 4.4369443529799146e-05, 'epoch': 0.6}\n",
      "{'loss': 5.2784, 'grad_norm': 1.2173254489898682, 'learning_rate': 4.420480737569971e-05, 'epoch': 0.61}\n",
      "{'loss': 5.2906, 'grad_norm': 1.1684532165527344, 'learning_rate': 4.404017122160027e-05, 'epoch': 0.62}\n",
      "{'loss': 5.3025, 'grad_norm': 1.1199374198913574, 'learning_rate': 4.387553506750082e-05, 'epoch': 0.63}\n",
      "{'loss': 5.2774, 'grad_norm': 1.1252669095993042, 'learning_rate': 4.3710898913401383e-05, 'epoch': 0.64}\n",
      "{'loss': 5.288, 'grad_norm': 1.135992169380188, 'learning_rate': 4.354626275930194e-05, 'epoch': 0.65}\n",
      "{'loss': 5.2423, 'grad_norm': 1.2881770133972168, 'learning_rate': 4.33816266052025e-05, 'epoch': 0.66}\n",
      "{'loss': 5.2597, 'grad_norm': 1.199745535850525, 'learning_rate': 4.3216990451103065e-05, 'epoch': 0.67}\n",
      "{'loss': 5.2415, 'grad_norm': 1.1572469472885132, 'learning_rate': 4.305235429700362e-05, 'epoch': 0.68}\n",
      "{'loss': 5.2718, 'grad_norm': 1.2711989879608154, 'learning_rate': 4.2887718142904184e-05, 'epoch': 0.68}\n",
      "{'loss': 5.2334, 'grad_norm': 1.1748969554901123, 'learning_rate': 4.272308198880474e-05, 'epoch': 0.69}\n",
      "{'loss': 5.2261, 'grad_norm': 1.2650811672210693, 'learning_rate': 4.25584458347053e-05, 'epoch': 0.7}\n",
      "{'loss': 5.1913, 'grad_norm': 1.1326992511749268, 'learning_rate': 4.239380968060586e-05, 'epoch': 0.71}\n",
      "{'loss': 5.2204, 'grad_norm': 1.2859127521514893, 'learning_rate': 4.222917352650642e-05, 'epoch': 0.72}\n",
      "{'loss': 5.1644, 'grad_norm': 1.2114582061767578, 'learning_rate': 4.206453737240698e-05, 'epoch': 0.73}\n",
      "{'loss': 5.1907, 'grad_norm': 1.3865121603012085, 'learning_rate': 4.189990121830754e-05, 'epoch': 0.74}\n",
      "{'loss': 5.1782, 'grad_norm': 1.4485503435134888, 'learning_rate': 4.1735265064208104e-05, 'epoch': 0.75}\n",
      "{'loss': 5.1721, 'grad_norm': 1.2274363040924072, 'learning_rate': 4.157062891010866e-05, 'epoch': 0.76}\n",
      "{'loss': 5.1578, 'grad_norm': 1.1913232803344727, 'learning_rate': 4.140599275600922e-05, 'epoch': 0.76}\n",
      "{'loss': 5.1588, 'grad_norm': 1.1764495372772217, 'learning_rate': 4.124135660190978e-05, 'epoch': 0.77}\n",
      "{'loss': 5.138, 'grad_norm': 1.2760459184646606, 'learning_rate': 4.107672044781034e-05, 'epoch': 0.78}\n",
      "{'loss': 5.1416, 'grad_norm': 1.248575210571289, 'learning_rate': 4.09120842937109e-05, 'epoch': 0.79}\n",
      "{'loss': 5.0944, 'grad_norm': 1.395334243774414, 'learning_rate': 4.074744813961146e-05, 'epoch': 0.8}\n",
      "{'loss': 5.1249, 'grad_norm': 1.2850102186203003, 'learning_rate': 4.058281198551202e-05, 'epoch': 0.81}\n",
      "{'loss': 5.0738, 'grad_norm': 1.473629117012024, 'learning_rate': 4.041817583141258e-05, 'epoch': 0.82}\n",
      "{'loss': 5.1401, 'grad_norm': 1.442339301109314, 'learning_rate': 4.025353967731314e-05, 'epoch': 0.83}\n",
      "{'loss': 5.1242, 'grad_norm': 1.52792227268219, 'learning_rate': 4.00889035232137e-05, 'epoch': 0.84}\n",
      "{'loss': 5.0916, 'grad_norm': 1.3267822265625, 'learning_rate': 3.992426736911426e-05, 'epoch': 0.84}\n",
      "{'loss': 5.0493, 'grad_norm': 1.338683009147644, 'learning_rate': 3.975963121501482e-05, 'epoch': 0.85}\n",
      "{'loss': 5.0832, 'grad_norm': 1.54911208152771, 'learning_rate': 3.959499506091538e-05, 'epoch': 0.86}\n",
      "{'loss': 5.0989, 'grad_norm': 1.473403811454773, 'learning_rate': 3.943035890681594e-05, 'epoch': 0.87}\n",
      "{'loss': 5.0604, 'grad_norm': 1.350206732749939, 'learning_rate': 3.92657227527165e-05, 'epoch': 0.88}\n",
      "{'loss': 5.0073, 'grad_norm': 1.30962073802948, 'learning_rate': 3.910108659861706e-05, 'epoch': 0.89}\n",
      "{'loss': 5.033, 'grad_norm': 1.6430158615112305, 'learning_rate': 3.893645044451762e-05, 'epoch': 0.9}\n",
      "{'loss': 5.0126, 'grad_norm': 1.2798100709915161, 'learning_rate': 3.877181429041818e-05, 'epoch': 0.91}\n",
      "{'loss': 5.0383, 'grad_norm': 1.3781015872955322, 'learning_rate': 3.8607178136318737e-05, 'epoch': 0.92}\n",
      "{'loss': 5.0614, 'grad_norm': 1.4126278162002563, 'learning_rate': 3.84425419822193e-05, 'epoch': 0.92}\n",
      "{'loss': 4.9514, 'grad_norm': 1.5056147575378418, 'learning_rate': 3.8277905828119855e-05, 'epoch': 0.93}\n",
      "{'loss': 5.0044, 'grad_norm': 1.5471025705337524, 'learning_rate': 3.811326967402042e-05, 'epoch': 0.94}\n",
      "{'loss': 4.9782, 'grad_norm': 1.3459399938583374, 'learning_rate': 3.794863351992098e-05, 'epoch': 0.95}\n",
      "{'loss': 5.0109, 'grad_norm': 1.3685293197631836, 'learning_rate': 3.778399736582154e-05, 'epoch': 0.96}\n",
      "{'loss': 4.9736, 'grad_norm': 1.3539719581604004, 'learning_rate': 3.76193612117221e-05, 'epoch': 0.97}\n",
      "{'loss': 5.0261, 'grad_norm': 1.5707237720489502, 'learning_rate': 3.7454725057622656e-05, 'epoch': 0.98}\n",
      "{'loss': 5.0025, 'grad_norm': 1.4463677406311035, 'learning_rate': 3.729008890352322e-05, 'epoch': 0.99}\n",
      "{'loss': 5.0535, 'grad_norm': 1.4711565971374512, 'learning_rate': 3.7125452749423775e-05, 'epoch': 1.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b8b924d86794c76b24c2061eac1a98f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1001 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 4.957149982452393, 'eval_accuracy': 0.2169728783902012, 'eval_runtime': 76.0873, 'eval_samples_per_second': 105.156, 'eval_steps_per_second': 13.156, 'epoch': 1.0}\n",
      "{'loss': 4.8993, 'grad_norm': 1.3653733730316162, 'learning_rate': 3.696081659532433e-05, 'epoch': 1.0}\n",
      "{'loss': 4.8834, 'grad_norm': 1.4774038791656494, 'learning_rate': 3.6796180441224894e-05, 'epoch': 1.01}\n",
      "{'loss': 4.9007, 'grad_norm': 1.4379853010177612, 'learning_rate': 3.663154428712545e-05, 'epoch': 1.02}\n",
      "{'loss': 4.8714, 'grad_norm': 1.4435831308364868, 'learning_rate': 3.646690813302601e-05, 'epoch': 1.03}\n",
      "{'loss': 4.9021, 'grad_norm': 1.6181260347366333, 'learning_rate': 3.630227197892657e-05, 'epoch': 1.04}\n",
      "{'loss': 4.9192, 'grad_norm': 1.494800090789795, 'learning_rate': 3.613763582482713e-05, 'epoch': 1.05}\n",
      "{'loss': 4.8974, 'grad_norm': 2.6939356327056885, 'learning_rate': 3.5972999670727694e-05, 'epoch': 1.06}\n",
      "{'loss': 4.963, 'grad_norm': 1.4140138626098633, 'learning_rate': 3.580836351662825e-05, 'epoch': 1.07}\n",
      "{'loss': 4.8773, 'grad_norm': 1.3485630750656128, 'learning_rate': 3.564372736252881e-05, 'epoch': 1.08}\n",
      "{'loss': 4.8696, 'grad_norm': 1.4951151609420776, 'learning_rate': 3.547909120842937e-05, 'epoch': 1.08}\n",
      "{'loss': 4.8978, 'grad_norm': 1.7047938108444214, 'learning_rate': 3.531445505432993e-05, 'epoch': 1.09}\n",
      "{'loss': 4.8382, 'grad_norm': 1.6638559103012085, 'learning_rate': 3.514981890023049e-05, 'epoch': 1.1}\n",
      "{'loss': 4.8815, 'grad_norm': 1.5207560062408447, 'learning_rate': 3.498518274613105e-05, 'epoch': 1.11}\n",
      "{'loss': 4.8556, 'grad_norm': 1.35409414768219, 'learning_rate': 3.4820546592031614e-05, 'epoch': 1.12}\n",
      "{'loss': 4.803, 'grad_norm': 1.5662429332733154, 'learning_rate': 3.465591043793217e-05, 'epoch': 1.13}\n",
      "{'loss': 4.7951, 'grad_norm': 1.662122368812561, 'learning_rate': 3.449127428383273e-05, 'epoch': 1.14}\n",
      "{'loss': 4.8152, 'grad_norm': 1.4856303930282593, 'learning_rate': 3.432663812973329e-05, 'epoch': 1.15}\n",
      "{'loss': 4.7962, 'grad_norm': 1.6326074600219727, 'learning_rate': 3.416200197563385e-05, 'epoch': 1.16}\n",
      "{'loss': 4.8564, 'grad_norm': 1.6111441850662231, 'learning_rate': 3.399736582153441e-05, 'epoch': 1.16}\n",
      "{'loss': 4.747, 'grad_norm': 1.5864267349243164, 'learning_rate': 3.383272966743497e-05, 'epoch': 1.17}\n",
      "{'loss': 4.8076, 'grad_norm': 1.5208265781402588, 'learning_rate': 3.3668093513335534e-05, 'epoch': 1.18}\n",
      "{'loss': 4.8095, 'grad_norm': 1.5489100217819214, 'learning_rate': 3.350345735923609e-05, 'epoch': 1.19}\n",
      "{'loss': 4.7641, 'grad_norm': 1.8204108476638794, 'learning_rate': 3.333882120513665e-05, 'epoch': 1.2}\n",
      "{'loss': 4.7658, 'grad_norm': 1.545945167541504, 'learning_rate': 3.317418505103721e-05, 'epoch': 1.21}\n",
      "{'loss': 4.8105, 'grad_norm': 1.6714030504226685, 'learning_rate': 3.300954889693777e-05, 'epoch': 1.22}\n",
      "{'loss': 4.7846, 'grad_norm': 1.8168423175811768, 'learning_rate': 3.284491274283833e-05, 'epoch': 1.23}\n",
      "{'loss': 4.7641, 'grad_norm': 1.8509114980697632, 'learning_rate': 3.268027658873889e-05, 'epoch': 1.24}\n",
      "{'loss': 4.7906, 'grad_norm': 1.681134581565857, 'learning_rate': 3.2515640434639446e-05, 'epoch': 1.24}\n",
      "{'loss': 4.7975, 'grad_norm': 1.5602340698242188, 'learning_rate': 3.235100428054001e-05, 'epoch': 1.25}\n",
      "{'loss': 4.7821, 'grad_norm': 1.623347520828247, 'learning_rate': 3.218636812644057e-05, 'epoch': 1.26}\n",
      "{'loss': 4.7265, 'grad_norm': 1.9641783237457275, 'learning_rate': 3.202173197234113e-05, 'epoch': 1.27}\n",
      "{'loss': 4.7221, 'grad_norm': 1.962636113166809, 'learning_rate': 3.185709581824169e-05, 'epoch': 1.28}\n",
      "{'loss': 4.7399, 'grad_norm': 1.9231404066085815, 'learning_rate': 3.169245966414225e-05, 'epoch': 1.29}\n",
      "{'loss': 4.707, 'grad_norm': 1.408921718597412, 'learning_rate': 3.152782351004281e-05, 'epoch': 1.3}\n",
      "{'loss': 4.764, 'grad_norm': 1.4177690744400024, 'learning_rate': 3.1363187355943366e-05, 'epoch': 1.31}\n",
      "{'loss': 4.7577, 'grad_norm': 1.5957190990447998, 'learning_rate': 3.119855120184393e-05, 'epoch': 1.32}\n",
      "{'loss': 4.667, 'grad_norm': 1.6137858629226685, 'learning_rate': 3.103391504774449e-05, 'epoch': 1.32}\n",
      "{'loss': 4.7084, 'grad_norm': 2.0900332927703857, 'learning_rate': 3.086927889364505e-05, 'epoch': 1.33}\n",
      "{'loss': 4.6616, 'grad_norm': 1.6454256772994995, 'learning_rate': 3.070464273954561e-05, 'epoch': 1.34}\n",
      "{'loss': 4.6672, 'grad_norm': 1.6279855966567993, 'learning_rate': 3.0540006585446166e-05, 'epoch': 1.35}\n",
      "{'loss': 4.7197, 'grad_norm': 1.7160922288894653, 'learning_rate': 3.0375370431346723e-05, 'epoch': 1.36}\n",
      "{'loss': 4.7086, 'grad_norm': 1.8108189105987549, 'learning_rate': 3.0210734277247282e-05, 'epoch': 1.37}\n",
      "{'loss': 4.6895, 'grad_norm': 1.685046672821045, 'learning_rate': 3.004609812314784e-05, 'epoch': 1.38}\n",
      "{'loss': 4.6985, 'grad_norm': 1.6361486911773682, 'learning_rate': 2.98814619690484e-05, 'epoch': 1.39}\n",
      "{'loss': 4.639, 'grad_norm': 1.6749613285064697, 'learning_rate': 2.9716825814948964e-05, 'epoch': 1.4}\n",
      "{'loss': 4.7094, 'grad_norm': 1.5922151803970337, 'learning_rate': 2.9552189660849523e-05, 'epoch': 1.4}\n",
      "{'loss': 4.7153, 'grad_norm': 1.5665174722671509, 'learning_rate': 2.9387553506750083e-05, 'epoch': 1.41}\n",
      "{'loss': 4.6414, 'grad_norm': 2.0264580249786377, 'learning_rate': 2.9222917352650642e-05, 'epoch': 1.42}\n",
      "{'loss': 4.7188, 'grad_norm': 1.818171739578247, 'learning_rate': 2.90582811985512e-05, 'epoch': 1.43}\n",
      "{'loss': 4.6517, 'grad_norm': 1.7088600397109985, 'learning_rate': 2.889364504445176e-05, 'epoch': 1.44}\n",
      "{'loss': 4.6057, 'grad_norm': 2.086190938949585, 'learning_rate': 2.872900889035232e-05, 'epoch': 1.45}\n",
      "{'loss': 4.5875, 'grad_norm': 1.6953229904174805, 'learning_rate': 2.856437273625288e-05, 'epoch': 1.46}\n",
      "{'loss': 4.6801, 'grad_norm': 1.7572556734085083, 'learning_rate': 2.8399736582153443e-05, 'epoch': 1.47}\n",
      "{'loss': 4.6358, 'grad_norm': 1.6181272268295288, 'learning_rate': 2.8235100428054002e-05, 'epoch': 1.48}\n",
      "{'loss': 4.5666, 'grad_norm': 1.9466805458068848, 'learning_rate': 2.807046427395456e-05, 'epoch': 1.48}\n",
      "{'loss': 4.6613, 'grad_norm': 2.4543793201446533, 'learning_rate': 2.790582811985512e-05, 'epoch': 1.49}\n",
      "{'loss': 4.6178, 'grad_norm': 1.6554107666015625, 'learning_rate': 2.774119196575568e-05, 'epoch': 1.5}\n",
      "{'loss': 4.6576, 'grad_norm': 1.7807337045669556, 'learning_rate': 2.757655581165624e-05, 'epoch': 1.51}\n",
      "{'loss': 4.6348, 'grad_norm': 1.6393810510635376, 'learning_rate': 2.74119196575568e-05, 'epoch': 1.52}\n",
      "{'loss': 4.6087, 'grad_norm': 1.5589855909347534, 'learning_rate': 2.724728350345736e-05, 'epoch': 1.53}\n",
      "{'loss': 4.5861, 'grad_norm': 1.9289544820785522, 'learning_rate': 2.708264734935792e-05, 'epoch': 1.54}\n",
      "{'loss': 4.6183, 'grad_norm': 2.1846375465393066, 'learning_rate': 2.691801119525848e-05, 'epoch': 1.55}\n",
      "{'loss': 4.5402, 'grad_norm': 1.8190040588378906, 'learning_rate': 2.675337504115904e-05, 'epoch': 1.56}\n",
      "{'loss': 4.5562, 'grad_norm': 1.6326746940612793, 'learning_rate': 2.65887388870596e-05, 'epoch': 1.56}\n",
      "{'loss': 4.5397, 'grad_norm': 1.879045844078064, 'learning_rate': 2.642410273296016e-05, 'epoch': 1.57}\n",
      "{'loss': 4.5226, 'grad_norm': 1.8459217548370361, 'learning_rate': 2.625946657886072e-05, 'epoch': 1.58}\n",
      "{'loss': 4.593, 'grad_norm': 1.8687827587127686, 'learning_rate': 2.609483042476128e-05, 'epoch': 1.59}\n",
      "{'loss': 4.4887, 'grad_norm': 1.9361088275909424, 'learning_rate': 2.593019427066184e-05, 'epoch': 1.6}\n",
      "{'loss': 4.6012, 'grad_norm': 2.0347678661346436, 'learning_rate': 2.57655581165624e-05, 'epoch': 1.61}\n",
      "{'loss': 4.571, 'grad_norm': 1.723789930343628, 'learning_rate': 2.560092196246296e-05, 'epoch': 1.62}\n",
      "{'loss': 4.5787, 'grad_norm': 1.8236076831817627, 'learning_rate': 2.543628580836352e-05, 'epoch': 1.63}\n",
      "{'loss': 4.5485, 'grad_norm': 1.8603923320770264, 'learning_rate': 2.527164965426408e-05, 'epoch': 1.64}\n",
      "{'loss': 4.5317, 'grad_norm': 1.928012728691101, 'learning_rate': 2.510701350016464e-05, 'epoch': 1.64}\n",
      "{'loss': 4.5481, 'grad_norm': 2.1910271644592285, 'learning_rate': 2.4942377346065194e-05, 'epoch': 1.65}\n",
      "{'loss': 4.5798, 'grad_norm': 1.6672786474227905, 'learning_rate': 2.4777741191965757e-05, 'epoch': 1.66}\n",
      "{'loss': 4.5436, 'grad_norm': 2.0598819255828857, 'learning_rate': 2.4613105037866317e-05, 'epoch': 1.67}\n",
      "{'loss': 4.4461, 'grad_norm': 1.8676042556762695, 'learning_rate': 2.4448468883766876e-05, 'epoch': 1.68}\n",
      "{'loss': 4.5279, 'grad_norm': 2.3574001789093018, 'learning_rate': 2.4283832729667436e-05, 'epoch': 1.69}\n",
      "{'loss': 4.4389, 'grad_norm': 2.130352735519409, 'learning_rate': 2.4119196575567995e-05, 'epoch': 1.7}\n",
      "{'loss': 4.4524, 'grad_norm': 2.0052897930145264, 'learning_rate': 2.3954560421468555e-05, 'epoch': 1.71}\n",
      "{'loss': 4.5323, 'grad_norm': 1.7837886810302734, 'learning_rate': 2.3789924267369114e-05, 'epoch': 1.72}\n",
      "{'loss': 4.553, 'grad_norm': 1.9908708333969116, 'learning_rate': 2.3625288113269677e-05, 'epoch': 1.72}\n",
      "{'loss': 4.4689, 'grad_norm': 1.7523146867752075, 'learning_rate': 2.3460651959170236e-05, 'epoch': 1.73}\n",
      "{'loss': 4.4961, 'grad_norm': 1.7291550636291504, 'learning_rate': 2.3296015805070796e-05, 'epoch': 1.74}\n",
      "{'loss': 4.4888, 'grad_norm': 2.1533052921295166, 'learning_rate': 2.3131379650971355e-05, 'epoch': 1.75}\n",
      "{'loss': 4.5131, 'grad_norm': 1.9543293714523315, 'learning_rate': 2.2966743496871915e-05, 'epoch': 1.76}\n",
      "{'loss': 4.4538, 'grad_norm': 4.2156596183776855, 'learning_rate': 2.2802107342772474e-05, 'epoch': 1.77}\n",
      "{'loss': 4.5049, 'grad_norm': 1.835220217704773, 'learning_rate': 2.2637471188673034e-05, 'epoch': 1.78}\n",
      "{'loss': 4.4284, 'grad_norm': 2.02307391166687, 'learning_rate': 2.2472835034573593e-05, 'epoch': 1.79}\n",
      "{'loss': 4.4446, 'grad_norm': 1.7700648307800293, 'learning_rate': 2.2308198880474156e-05, 'epoch': 1.8}\n",
      "{'loss': 4.5272, 'grad_norm': 1.6484451293945312, 'learning_rate': 2.2143562726374715e-05, 'epoch': 1.8}\n",
      "{'loss': 4.4403, 'grad_norm': 1.8010808229446411, 'learning_rate': 2.1978926572275275e-05, 'epoch': 1.81}\n",
      "{'loss': 4.4812, 'grad_norm': 2.0002143383026123, 'learning_rate': 2.181429041817583e-05, 'epoch': 1.82}\n",
      "{'loss': 4.3571, 'grad_norm': 1.9937148094177246, 'learning_rate': 2.164965426407639e-05, 'epoch': 1.83}\n",
      "{'loss': 4.4129, 'grad_norm': 2.1688485145568848, 'learning_rate': 2.148501810997695e-05, 'epoch': 1.84}\n",
      "{'loss': 4.4472, 'grad_norm': 2.5233073234558105, 'learning_rate': 2.1320381955877512e-05, 'epoch': 1.85}\n",
      "{'loss': 4.4606, 'grad_norm': 2.075115919113159, 'learning_rate': 2.1155745801778072e-05, 'epoch': 1.86}\n",
      "{'loss': 4.3794, 'grad_norm': 1.9001195430755615, 'learning_rate': 2.099110964767863e-05, 'epoch': 1.87}\n",
      "{'loss': 4.4662, 'grad_norm': 2.232886791229248, 'learning_rate': 2.082647349357919e-05, 'epoch': 1.88}\n",
      "{'loss': 4.3773, 'grad_norm': 2.046858549118042, 'learning_rate': 2.066183733947975e-05, 'epoch': 1.88}\n",
      "{'loss': 4.4018, 'grad_norm': 1.9070874452590942, 'learning_rate': 2.049720118538031e-05, 'epoch': 1.89}\n",
      "{'loss': 4.462, 'grad_norm': 2.198991298675537, 'learning_rate': 2.033256503128087e-05, 'epoch': 1.9}\n",
      "{'loss': 4.284, 'grad_norm': 2.391381025314331, 'learning_rate': 2.016792887718143e-05, 'epoch': 1.91}\n",
      "{'loss': 4.4199, 'grad_norm': 2.237800121307373, 'learning_rate': 2.000329272308199e-05, 'epoch': 1.92}\n",
      "{'loss': 4.4373, 'grad_norm': 2.134345531463623, 'learning_rate': 1.983865656898255e-05, 'epoch': 1.93}\n",
      "{'loss': 4.3576, 'grad_norm': 2.0373694896698, 'learning_rate': 1.967402041488311e-05, 'epoch': 1.94}\n",
      "{'loss': 4.4074, 'grad_norm': 2.308903932571411, 'learning_rate': 1.950938426078367e-05, 'epoch': 1.95}\n",
      "{'loss': 4.3504, 'grad_norm': 1.865470290184021, 'learning_rate': 1.934474810668423e-05, 'epoch': 1.96}\n",
      "{'loss': 4.3983, 'grad_norm': 1.9790452718734741, 'learning_rate': 1.918011195258479e-05, 'epoch': 1.96}\n",
      "{'loss': 4.3363, 'grad_norm': 1.9545035362243652, 'learning_rate': 1.9015475798485348e-05, 'epoch': 1.97}\n",
      "{'loss': 4.4049, 'grad_norm': 1.8681645393371582, 'learning_rate': 1.8850839644385908e-05, 'epoch': 1.98}\n",
      "{'loss': 4.3726, 'grad_norm': 2.131404161453247, 'learning_rate': 1.868620349028647e-05, 'epoch': 1.99}\n",
      "{'loss': 4.4188, 'grad_norm': 1.780598759651184, 'learning_rate': 1.8521567336187026e-05, 'epoch': 2.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63f8b853e6e14c2385096641777b59d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1001 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 4.412487506866455, 'eval_accuracy': 0.2583427071616048, 'eval_runtime': 88.7081, 'eval_samples_per_second': 90.195, 'eval_steps_per_second': 11.284, 'epoch': 2.0}\n",
      "{'loss': 4.2468, 'grad_norm': 1.8213180303573608, 'learning_rate': 1.8356931182087586e-05, 'epoch': 2.01}\n",
      "{'loss': 4.3523, 'grad_norm': 2.8117318153381348, 'learning_rate': 1.8192295027988145e-05, 'epoch': 2.02}\n",
      "{'loss': 4.3152, 'grad_norm': 1.7463642358779907, 'learning_rate': 1.8027658873888705e-05, 'epoch': 2.03}\n",
      "{'loss': 4.3509, 'grad_norm': 1.9505913257598877, 'learning_rate': 1.7863022719789264e-05, 'epoch': 2.04}\n",
      "{'loss': 4.2851, 'grad_norm': 2.282400369644165, 'learning_rate': 1.7698386565689827e-05, 'epoch': 2.04}\n",
      "{'loss': 4.2536, 'grad_norm': 2.147862195968628, 'learning_rate': 1.7533750411590387e-05, 'epoch': 2.05}\n",
      "{'loss': 4.3518, 'grad_norm': 2.477461099624634, 'learning_rate': 1.7369114257490946e-05, 'epoch': 2.06}\n",
      "{'loss': 4.3363, 'grad_norm': 1.6753101348876953, 'learning_rate': 1.7204478103391505e-05, 'epoch': 2.07}\n",
      "{'loss': 4.3399, 'grad_norm': 1.9577041864395142, 'learning_rate': 1.7039841949292065e-05, 'epoch': 2.08}\n",
      "{'loss': 4.3007, 'grad_norm': 2.1194701194763184, 'learning_rate': 1.6875205795192624e-05, 'epoch': 2.09}\n",
      "{'loss': 4.2449, 'grad_norm': 1.779801845550537, 'learning_rate': 1.6710569641093184e-05, 'epoch': 2.1}\n",
      "{'loss': 4.2851, 'grad_norm': 2.6363956928253174, 'learning_rate': 1.6545933486993743e-05, 'epoch': 2.11}\n",
      "{'loss': 4.23, 'grad_norm': 2.7754321098327637, 'learning_rate': 1.6381297332894306e-05, 'epoch': 2.12}\n",
      "{'loss': 4.221, 'grad_norm': 2.2485499382019043, 'learning_rate': 1.6216661178794866e-05, 'epoch': 2.12}\n",
      "{'loss': 4.3667, 'grad_norm': 2.7162868976593018, 'learning_rate': 1.6052025024695425e-05, 'epoch': 2.13}\n",
      "{'loss': 4.3149, 'grad_norm': 2.1848835945129395, 'learning_rate': 1.5887388870595984e-05, 'epoch': 2.14}\n",
      "{'loss': 4.3353, 'grad_norm': 2.0698418617248535, 'learning_rate': 1.5722752716496544e-05, 'epoch': 2.15}\n",
      "{'loss': 4.1865, 'grad_norm': 2.214423894882202, 'learning_rate': 1.5558116562397103e-05, 'epoch': 2.16}\n",
      "{'loss': 4.3212, 'grad_norm': 2.677985668182373, 'learning_rate': 1.5393480408297663e-05, 'epoch': 2.17}\n",
      "{'loss': 4.3129, 'grad_norm': 2.016322374343872, 'learning_rate': 1.5228844254198224e-05, 'epoch': 2.18}\n",
      "{'loss': 4.3109, 'grad_norm': 1.9704530239105225, 'learning_rate': 1.5064208100098782e-05, 'epoch': 2.19}\n",
      "{'loss': 4.2537, 'grad_norm': 1.8943309783935547, 'learning_rate': 1.4899571945999341e-05, 'epoch': 2.2}\n",
      "{'loss': 4.2847, 'grad_norm': 2.176987409591675, 'learning_rate': 1.47349357918999e-05, 'epoch': 2.2}\n",
      "{'loss': 4.2501, 'grad_norm': 2.449345827102661, 'learning_rate': 1.457029963780046e-05, 'epoch': 2.21}\n",
      "{'loss': 4.2694, 'grad_norm': 2.307283878326416, 'learning_rate': 1.4405663483701021e-05, 'epoch': 2.22}\n",
      "{'loss': 4.3147, 'grad_norm': 2.142667055130005, 'learning_rate': 1.424102732960158e-05, 'epoch': 2.23}\n",
      "{'loss': 4.2882, 'grad_norm': 2.0259907245635986, 'learning_rate': 1.407639117550214e-05, 'epoch': 2.24}\n",
      "{'loss': 4.2451, 'grad_norm': 2.038130283355713, 'learning_rate': 1.3911755021402701e-05, 'epoch': 2.25}\n",
      "{'loss': 4.3035, 'grad_norm': 2.459458827972412, 'learning_rate': 1.374711886730326e-05, 'epoch': 2.26}\n",
      "{'loss': 4.3372, 'grad_norm': 2.22371244430542, 'learning_rate': 1.358248271320382e-05, 'epoch': 2.27}\n",
      "{'loss': 4.2165, 'grad_norm': 2.2588462829589844, 'learning_rate': 1.341784655910438e-05, 'epoch': 2.28}\n",
      "{'loss': 4.2833, 'grad_norm': 2.9824864864349365, 'learning_rate': 1.325321040500494e-05, 'epoch': 2.28}\n",
      "{'loss': 4.1877, 'grad_norm': 1.9008567333221436, 'learning_rate': 1.30885742509055e-05, 'epoch': 2.29}\n",
      "{'loss': 4.2832, 'grad_norm': 2.397346258163452, 'learning_rate': 1.292393809680606e-05, 'epoch': 2.3}\n",
      "{'loss': 4.2284, 'grad_norm': 2.0876286029815674, 'learning_rate': 1.2759301942706619e-05, 'epoch': 2.31}\n",
      "{'loss': 4.2022, 'grad_norm': 2.7684569358825684, 'learning_rate': 1.259466578860718e-05, 'epoch': 2.32}\n",
      "{'loss': 4.2136, 'grad_norm': 2.1218128204345703, 'learning_rate': 1.2430029634507738e-05, 'epoch': 2.33}\n",
      "{'loss': 4.2751, 'grad_norm': 2.4325311183929443, 'learning_rate': 1.2265393480408297e-05, 'epoch': 2.34}\n",
      "{'loss': 4.3004, 'grad_norm': 2.031203269958496, 'learning_rate': 1.2100757326308859e-05, 'epoch': 2.35}\n",
      "{'loss': 4.2061, 'grad_norm': 2.1571004390716553, 'learning_rate': 1.1936121172209418e-05, 'epoch': 2.36}\n",
      "{'loss': 4.2336, 'grad_norm': 2.597979784011841, 'learning_rate': 1.1771485018109977e-05, 'epoch': 2.36}\n",
      "{'loss': 4.1502, 'grad_norm': 2.0861475467681885, 'learning_rate': 1.1606848864010537e-05, 'epoch': 2.37}\n",
      "{'loss': 4.2704, 'grad_norm': 3.247725009918213, 'learning_rate': 1.1442212709911098e-05, 'epoch': 2.38}\n",
      "{'loss': 4.2578, 'grad_norm': 2.1407084465026855, 'learning_rate': 1.1277576555811657e-05, 'epoch': 2.39}\n",
      "{'loss': 4.2021, 'grad_norm': 2.144508123397827, 'learning_rate': 1.1112940401712217e-05, 'epoch': 2.4}\n",
      "{'loss': 4.2265, 'grad_norm': 2.351987600326538, 'learning_rate': 1.0948304247612776e-05, 'epoch': 2.41}\n",
      "{'loss': 4.1552, 'grad_norm': 2.145186424255371, 'learning_rate': 1.0783668093513336e-05, 'epoch': 2.42}\n",
      "{'loss': 4.203, 'grad_norm': 2.1409478187561035, 'learning_rate': 1.0619031939413895e-05, 'epoch': 2.43}\n",
      "{'loss': 4.2069, 'grad_norm': 2.0594663619995117, 'learning_rate': 1.0454395785314455e-05, 'epoch': 2.44}\n",
      "{'loss': 4.1778, 'grad_norm': 2.2129526138305664, 'learning_rate': 1.0289759631215016e-05, 'epoch': 2.44}\n",
      "{'loss': 4.2176, 'grad_norm': 2.8110995292663574, 'learning_rate': 1.0125123477115575e-05, 'epoch': 2.45}\n",
      "{'loss': 4.225, 'grad_norm': 2.1255743503570557, 'learning_rate': 9.960487323016135e-06, 'epoch': 2.46}\n",
      "{'loss': 4.1739, 'grad_norm': 2.0709266662597656, 'learning_rate': 9.795851168916694e-06, 'epoch': 2.47}\n",
      "{'loss': 4.2198, 'grad_norm': 2.3680856227874756, 'learning_rate': 9.631215014817255e-06, 'epoch': 2.48}\n",
      "{'loss': 4.2433, 'grad_norm': 2.3391597270965576, 'learning_rate': 9.466578860717815e-06, 'epoch': 2.49}\n",
      "{'loss': 4.1767, 'grad_norm': 2.9973559379577637, 'learning_rate': 9.301942706618374e-06, 'epoch': 2.5}\n",
      "{'loss': 4.1833, 'grad_norm': 2.691636085510254, 'learning_rate': 9.137306552518934e-06, 'epoch': 2.51}\n",
      "{'loss': 4.1138, 'grad_norm': 2.0133891105651855, 'learning_rate': 8.972670398419493e-06, 'epoch': 2.51}\n",
      "{'loss': 4.2714, 'grad_norm': 1.9475247859954834, 'learning_rate': 8.808034244320053e-06, 'epoch': 2.52}\n",
      "{'loss': 4.1424, 'grad_norm': 2.1982028484344482, 'learning_rate': 8.643398090220612e-06, 'epoch': 2.53}\n",
      "{'loss': 4.193, 'grad_norm': 1.9643152952194214, 'learning_rate': 8.478761936121173e-06, 'epoch': 2.54}\n",
      "{'loss': 4.1615, 'grad_norm': 2.003878355026245, 'learning_rate': 8.314125782021733e-06, 'epoch': 2.55}\n",
      "{'loss': 4.1564, 'grad_norm': 3.05075740814209, 'learning_rate': 8.149489627922292e-06, 'epoch': 2.56}\n",
      "{'loss': 4.1475, 'grad_norm': 2.157576322555542, 'learning_rate': 7.984853473822852e-06, 'epoch': 2.57}\n",
      "{'loss': 4.1935, 'grad_norm': 2.1238512992858887, 'learning_rate': 7.820217319723413e-06, 'epoch': 2.58}\n",
      "{'loss': 4.126, 'grad_norm': 2.4104154109954834, 'learning_rate': 7.655581165623972e-06, 'epoch': 2.59}\n",
      "{'loss': 4.2632, 'grad_norm': 2.530040979385376, 'learning_rate': 7.490945011524531e-06, 'epoch': 2.59}\n",
      "{'loss': 4.2442, 'grad_norm': 2.406911611557007, 'learning_rate': 7.32630885742509e-06, 'epoch': 2.6}\n",
      "{'loss': 4.2093, 'grad_norm': 2.688847780227661, 'learning_rate': 7.1616727033256504e-06, 'epoch': 2.61}\n",
      "{'loss': 4.2381, 'grad_norm': 2.0510928630828857, 'learning_rate': 6.99703654922621e-06, 'epoch': 2.62}\n",
      "{'loss': 4.18, 'grad_norm': 2.228199005126953, 'learning_rate': 6.83240039512677e-06, 'epoch': 2.63}\n",
      "{'loss': 4.204, 'grad_norm': 2.723302125930786, 'learning_rate': 6.66776424102733e-06, 'epoch': 2.64}\n",
      "{'loss': 4.1708, 'grad_norm': 2.4656529426574707, 'learning_rate': 6.50312808692789e-06, 'epoch': 2.65}\n",
      "{'loss': 4.0611, 'grad_norm': 2.0465149879455566, 'learning_rate': 6.338491932828449e-06, 'epoch': 2.66}\n",
      "{'loss': 4.219, 'grad_norm': 2.151592254638672, 'learning_rate': 6.173855778729009e-06, 'epoch': 2.67}\n",
      "{'loss': 4.1574, 'grad_norm': 2.6070990562438965, 'learning_rate': 6.009219624629569e-06, 'epoch': 2.67}\n",
      "{'loss': 4.2584, 'grad_norm': 2.1416687965393066, 'learning_rate': 5.8445834705301286e-06, 'epoch': 2.68}\n",
      "{'loss': 4.2212, 'grad_norm': 2.493741273880005, 'learning_rate': 5.679947316430689e-06, 'epoch': 2.69}\n",
      "{'loss': 4.1324, 'grad_norm': 2.178687572479248, 'learning_rate': 5.515311162331248e-06, 'epoch': 2.7}\n",
      "{'loss': 4.2382, 'grad_norm': 2.607297897338867, 'learning_rate': 5.350675008231808e-06, 'epoch': 2.71}\n",
      "{'loss': 3.9919, 'grad_norm': 2.4945454597473145, 'learning_rate': 5.186038854132367e-06, 'epoch': 2.72}\n",
      "{'loss': 4.1217, 'grad_norm': 2.2347328662872314, 'learning_rate': 5.0214027000329275e-06, 'epoch': 2.73}\n",
      "{'loss': 4.064, 'grad_norm': 2.326517105102539, 'learning_rate': 4.856766545933488e-06, 'epoch': 2.74}\n",
      "{'loss': 4.131, 'grad_norm': 3.2224950790405273, 'learning_rate': 4.692130391834047e-06, 'epoch': 2.75}\n",
      "{'loss': 4.206, 'grad_norm': 3.053706169128418, 'learning_rate': 4.527494237734607e-06, 'epoch': 2.75}\n",
      "{'loss': 4.2881, 'grad_norm': 2.3909480571746826, 'learning_rate': 4.362858083635166e-06, 'epoch': 2.76}\n",
      "{'loss': 4.1475, 'grad_norm': 2.4445784091949463, 'learning_rate': 4.1982219295357264e-06, 'epoch': 2.77}\n",
      "{'loss': 4.1779, 'grad_norm': 2.304943323135376, 'learning_rate': 4.033585775436286e-06, 'epoch': 2.78}\n",
      "{'loss': 4.1397, 'grad_norm': 2.3117282390594482, 'learning_rate': 3.868949621336846e-06, 'epoch': 2.79}\n",
      "{'loss': 4.2405, 'grad_norm': 2.443709373474121, 'learning_rate': 3.7043134672374052e-06, 'epoch': 2.8}\n",
      "{'loss': 4.1358, 'grad_norm': 2.0286383628845215, 'learning_rate': 3.539677313137965e-06, 'epoch': 2.81}\n",
      "{'loss': 4.1589, 'grad_norm': 2.2737700939178467, 'learning_rate': 3.375041159038525e-06, 'epoch': 2.82}\n",
      "{'loss': 4.1953, 'grad_norm': 2.6672463417053223, 'learning_rate': 3.210405004939085e-06, 'epoch': 2.83}\n",
      "{'loss': 4.2151, 'grad_norm': 2.1402323246002197, 'learning_rate': 3.0457688508396443e-06, 'epoch': 2.83}\n",
      "{'loss': 4.3077, 'grad_norm': 2.481356143951416, 'learning_rate': 2.881132696740204e-06, 'epoch': 2.84}\n",
      "{'loss': 4.1325, 'grad_norm': 2.170210123062134, 'learning_rate': 2.716496542640764e-06, 'epoch': 2.85}\n",
      "{'loss': 4.1478, 'grad_norm': 2.3700592517852783, 'learning_rate': 2.551860388541324e-06, 'epoch': 2.86}\n",
      "{'loss': 4.1955, 'grad_norm': 2.932849645614624, 'learning_rate': 2.3872242344418838e-06, 'epoch': 2.87}\n",
      "{'loss': 4.1396, 'grad_norm': 2.0267951488494873, 'learning_rate': 2.2225880803424432e-06, 'epoch': 2.88}\n",
      "{'loss': 4.1476, 'grad_norm': 2.3862361907958984, 'learning_rate': 2.057951926243003e-06, 'epoch': 2.89}\n",
      "{'loss': 4.2036, 'grad_norm': 2.5772182941436768, 'learning_rate': 1.8933157721435627e-06, 'epoch': 2.9}\n",
      "{'loss': 4.1773, 'grad_norm': 3.2005748748779297, 'learning_rate': 1.7286796180441226e-06, 'epoch': 2.91}\n",
      "{'loss': 4.1281, 'grad_norm': 3.4535269737243652, 'learning_rate': 1.5640434639446825e-06, 'epoch': 2.91}\n",
      "{'loss': 4.1085, 'grad_norm': 2.5282809734344482, 'learning_rate': 1.3994073098452421e-06, 'epoch': 2.92}\n",
      "{'loss': 4.187, 'grad_norm': 3.1048123836517334, 'learning_rate': 1.2347711557458018e-06, 'epoch': 2.93}\n",
      "{'loss': 4.0735, 'grad_norm': 2.691267251968384, 'learning_rate': 1.0701350016463617e-06, 'epoch': 2.94}\n",
      "{'loss': 4.1548, 'grad_norm': 2.498093843460083, 'learning_rate': 9.054988475469213e-07, 'epoch': 2.95}\n",
      "{'loss': 4.1543, 'grad_norm': 2.129580020904541, 'learning_rate': 7.408626934474811e-07, 'epoch': 2.96}\n",
      "{'loss': 4.17, 'grad_norm': 2.302260637283325, 'learning_rate': 5.762265393480408e-07, 'epoch': 2.97}\n",
      "{'loss': 4.1334, 'grad_norm': 2.2644286155700684, 'learning_rate': 4.1159038524860064e-07, 'epoch': 2.98}\n",
      "{'loss': 4.1504, 'grad_norm': 2.605426788330078, 'learning_rate': 2.469542311491604e-07, 'epoch': 2.99}\n",
      "{'loss': 4.1784, 'grad_norm': 2.928342580795288, 'learning_rate': 8.231807704972012e-08, 'epoch': 2.99}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "78ffe4e9fef941b8bd382fd2889cc2c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1001 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 4.227635860443115, 'eval_accuracy': 0.27546556680414946, 'eval_runtime': 76.5679, 'eval_samples_per_second': 104.495, 'eval_steps_per_second': 13.073, 'epoch': 3.0}\n",
      "{'train_runtime': 6798.0282, 'train_samples_per_second': 31.775, 'train_steps_per_second': 0.496, 'train_loss': 4.755938376532661, 'epoch': 3.0}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=3375, training_loss=4.755938376532661, metrics={'train_runtime': 6798.0282, 'train_samples_per_second': 31.775, 'train_steps_per_second': 0.496, 'total_flos': 1.678650555446478e+19, 'train_loss': 4.755938376532661, 'epoch': 2.9993334814485673})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/n1hal/Weeds_Classification/commit/39a05bb9fc461265eb4e7d40d2a9ef2560e55299', commit_message='End of training', commit_description='', oid='39a05bb9fc461265eb4e7d40d2a9ef2560e55299', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.push_to_hub()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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",
      "image/png": "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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x256>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = load_dataset(\"n1hal/Capstone_Weeds\", split=\"validation\")\n",
    "image = ds[\"image\"][0]\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'Abelmoschus moschatus Medik.', 'score': 0.9511571526527405},\n",
       " {'label': 'Abelmoschus ficulneus L. Wight Arn.', 'score': 0.9086750149726868},\n",
       " {'label': 'Hibiscus vitifolius Linn.', 'score': 0.8956620097160339},\n",
       " {'label': 'Urena lobata L.', 'score': 0.8885037899017334},\n",
       " {'label': 'Hibiscus panduriformis Burm. f.', 'score': 0.887142539024353}]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "classifier = pipeline(\"image-classification\", model=\"Weeds_Classification\", device=0)\n",
    "classifier(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "337"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28301335c86642908e4d9850b30bba56",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1001 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 4.233978748321533,\n",
       " 'eval_accuracy': 0.27034120734908135,\n",
       " 'eval_runtime': 95.2531,\n",
       " 'eval_samples_per_second': 83.997,\n",
       " 'eval_steps_per_second': 10.509,\n",
       " 'epoch': 2.9993334814485673}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'loss': 5.8294,\n",
       "  'grad_norm': 0.9497740864753723,\n",
       "  'learning_rate': 1.4792899408284024e-06,\n",
       "  'epoch': 0.008886914019106865,\n",
       "  'step': 10},\n",
       " {'loss': 5.8243,\n",
       "  'grad_norm': 0.9087894558906555,\n",
       "  'learning_rate': 2.9585798816568047e-06,\n",
       "  'epoch': 0.01777382803821373,\n",
       "  'step': 20},\n",
       " {'loss': 5.827,\n",
       "  'grad_norm': 0.9352946877479553,\n",
       "  'learning_rate': 4.437869822485207e-06,\n",
       "  'epoch': 0.026660742057320594,\n",
       "  'step': 30},\n",
       " {'loss': 5.8183,\n",
       "  'grad_norm': 0.9707036018371582,\n",
       "  'learning_rate': 5.917159763313609e-06,\n",
       "  'epoch': 0.03554765607642746,\n",
       "  'step': 40},\n",
       " {'loss': 5.8326,\n",
       "  'grad_norm': 0.9326181411743164,\n",
       "  'learning_rate': 7.396449704142013e-06,\n",
       "  'epoch': 0.04443457009553432,\n",
       "  'step': 50},\n",
       " {'loss': 5.8168,\n",
       "  'grad_norm': 0.9484497308731079,\n",
       "  'learning_rate': 8.875739644970414e-06,\n",
       "  'epoch': 0.05332148411464119,\n",
       "  'step': 60},\n",
       " {'loss': 5.8207,\n",
       "  'grad_norm': 0.9521200656890869,\n",
       "  'learning_rate': 1.0355029585798817e-05,\n",
       "  'epoch': 0.06220839813374806,\n",
       "  'step': 70},\n",
       " {'loss': 5.817,\n",
       "  'grad_norm': 0.8906506896018982,\n",
       "  'learning_rate': 1.1834319526627219e-05,\n",
       "  'epoch': 0.07109531215285492,\n",
       "  'step': 80},\n",
       " {'loss': 5.8186,\n",
       "  'grad_norm': 0.995785117149353,\n",
       "  'learning_rate': 1.3313609467455624e-05,\n",
       "  'epoch': 0.07998222617196178,\n",
       "  'step': 90},\n",
       " {'loss': 5.8132,\n",
       "  'grad_norm': 0.9234928488731384,\n",
       "  'learning_rate': 1.4792899408284025e-05,\n",
       "  'epoch': 0.08886914019106865,\n",
       "  'step': 100},\n",
       " {'loss': 5.8128,\n",
       "  'grad_norm': 0.9095359444618225,\n",
       "  'learning_rate': 1.6272189349112425e-05,\n",
       "  'epoch': 0.09775605421017551,\n",
       "  'step': 110},\n",
       " {'loss': 5.8138,\n",
       "  'grad_norm': 0.9514939188957214,\n",
       "  'learning_rate': 1.7751479289940828e-05,\n",
       "  'epoch': 0.10664296822928238,\n",
       "  'step': 120},\n",
       " {'loss': 5.8066,\n",
       "  'grad_norm': 0.9680359959602356,\n",
       "  'learning_rate': 1.923076923076923e-05,\n",
       "  'epoch': 0.11552988224838924,\n",
       "  'step': 130},\n",
       " {'loss': 5.811,\n",
       "  'grad_norm': 0.9012009501457214,\n",
       "  'learning_rate': 2.0710059171597635e-05,\n",
       "  'epoch': 0.12441679626749612,\n",
       "  'step': 140},\n",
       " {'loss': 5.8024,\n",
       "  'grad_norm': 0.9819561839103699,\n",
       "  'learning_rate': 2.2189349112426034e-05,\n",
       "  'epoch': 0.13330371028660298,\n",
       "  'step': 150},\n",
       " {'loss': 5.8035,\n",
       "  'grad_norm': 0.9477075338363647,\n",
       "  'learning_rate': 2.3668639053254438e-05,\n",
       "  'epoch': 0.14219062430570983,\n",
       "  'step': 160},\n",
       " {'loss': 5.7995,\n",
       "  'grad_norm': 0.9122418165206909,\n",
       "  'learning_rate': 2.514792899408284e-05,\n",
       "  'epoch': 0.1510775383248167,\n",
       "  'step': 170},\n",
       " {'loss': 5.796,\n",
       "  'grad_norm': 0.8803086876869202,\n",
       "  'learning_rate': 2.6627218934911247e-05,\n",
       "  'epoch': 0.15996445234392356,\n",
       "  'step': 180},\n",
       " {'loss': 5.7887,\n",
       "  'grad_norm': 1.0798852443695068,\n",
       "  'learning_rate': 2.8106508875739644e-05,\n",
       "  'epoch': 0.16885136636303044,\n",
       "  'step': 190},\n",
       " {'loss': 5.7829,\n",
       "  'grad_norm': 0.9175708293914795,\n",
       "  'learning_rate': 2.958579881656805e-05,\n",
       "  'epoch': 0.1777382803821373,\n",
       "  'step': 200},\n",
       " {'loss': 5.7806,\n",
       "  'grad_norm': 0.9696438908576965,\n",
       "  'learning_rate': 3.106508875739645e-05,\n",
       "  'epoch': 0.18662519440124417,\n",
       "  'step': 210},\n",
       " {'loss': 5.7695,\n",
       "  'grad_norm': 1.0633084774017334,\n",
       "  'learning_rate': 3.254437869822485e-05,\n",
       "  'epoch': 0.19551210842035102,\n",
       "  'step': 220},\n",
       " {'loss': 5.7616,\n",
       "  'grad_norm': 0.9608509540557861,\n",
       "  'learning_rate': 3.402366863905326e-05,\n",
       "  'epoch': 0.2043990224394579,\n",
       "  'step': 230},\n",
       " {'loss': 5.7628,\n",
       "  'grad_norm': 0.9794811606407166,\n",
       "  'learning_rate': 3.5502958579881656e-05,\n",
       "  'epoch': 0.21328593645856475,\n",
       "  'step': 240},\n",
       " {'loss': 5.7523,\n",
       "  'grad_norm': 0.943600594997406,\n",
       "  'learning_rate': 3.698224852071006e-05,\n",
       "  'epoch': 0.22217285047767163,\n",
       "  'step': 250},\n",
       " {'loss': 5.7485,\n",
       "  'grad_norm': 0.9633746147155762,\n",
       "  'learning_rate': 3.846153846153846e-05,\n",
       "  'epoch': 0.23105976449677848,\n",
       "  'step': 260},\n",
       " {'loss': 5.7395,\n",
       "  'grad_norm': 0.9549353718757629,\n",
       "  'learning_rate': 3.9940828402366866e-05,\n",
       "  'epoch': 0.23994667851588536,\n",
       "  'step': 270},\n",
       " {'loss': 5.7339,\n",
       "  'grad_norm': 0.9791706800460815,\n",
       "  'learning_rate': 4.142011834319527e-05,\n",
       "  'epoch': 0.24883359253499224,\n",
       "  'step': 280},\n",
       " {'loss': 5.7144,\n",
       "  'grad_norm': 0.9533138275146484,\n",
       "  'learning_rate': 4.289940828402367e-05,\n",
       "  'epoch': 0.2577205065540991,\n",
       "  'step': 290},\n",
       " {'loss': 5.7006,\n",
       "  'grad_norm': 0.9881207346916199,\n",
       "  'learning_rate': 4.437869822485207e-05,\n",
       "  'epoch': 0.26660742057320597,\n",
       "  'step': 300},\n",
       " {'loss': 5.6986,\n",
       "  'grad_norm': 0.987064778804779,\n",
       "  'learning_rate': 4.585798816568048e-05,\n",
       "  'epoch': 0.2754943345923128,\n",
       "  'step': 310},\n",
       " {'loss': 5.6959,\n",
       "  'grad_norm': 1.0180878639221191,\n",
       "  'learning_rate': 4.7337278106508875e-05,\n",
       "  'epoch': 0.28438124861141967,\n",
       "  'step': 320},\n",
       " {'loss': 5.6742,\n",
       "  'grad_norm': 0.989738941192627,\n",
       "  'learning_rate': 4.881656804733728e-05,\n",
       "  'epoch': 0.2932681626305266,\n",
       "  'step': 330},\n",
       " {'loss': 5.6803,\n",
       "  'grad_norm': 1.0162245035171509,\n",
       "  'learning_rate': 4.996707276918011e-05,\n",
       "  'epoch': 0.3021550766496334,\n",
       "  'step': 340},\n",
       " {'loss': 5.669,\n",
       "  'grad_norm': 1.0447462797164917,\n",
       "  'learning_rate': 4.9802436615080674e-05,\n",
       "  'epoch': 0.3110419906687403,\n",
       "  'step': 350},\n",
       " {'loss': 5.6301,\n",
       "  'grad_norm': 1.0307732820510864,\n",
       "  'learning_rate': 4.963780046098123e-05,\n",
       "  'epoch': 0.3199289046878471,\n",
       "  'step': 360},\n",
       " {'loss': 5.6523,\n",
       "  'grad_norm': 0.9696402549743652,\n",
       "  'learning_rate': 4.947316430688179e-05,\n",
       "  'epoch': 0.32881581870695403,\n",
       "  'step': 370},\n",
       " {'loss': 5.6303,\n",
       "  'grad_norm': 0.9930351376533508,\n",
       "  'learning_rate': 4.930852815278235e-05,\n",
       "  'epoch': 0.3377027327260609,\n",
       "  'step': 380},\n",
       " {'loss': 5.6219,\n",
       "  'grad_norm': 1.0661391019821167,\n",
       "  'learning_rate': 4.914389199868291e-05,\n",
       "  'epoch': 0.34658964674516773,\n",
       "  'step': 390},\n",
       " {'loss': 5.6131,\n",
       "  'grad_norm': 1.0188394784927368,\n",
       "  'learning_rate': 4.8979255844583474e-05,\n",
       "  'epoch': 0.3554765607642746,\n",
       "  'step': 400},\n",
       " {'loss': 5.5867,\n",
       "  'grad_norm': 0.9948827028274536,\n",
       "  'learning_rate': 4.881461969048403e-05,\n",
       "  'epoch': 0.3643634747833815,\n",
       "  'step': 410},\n",
       " {'loss': 5.5683,\n",
       "  'grad_norm': 1.0241193771362305,\n",
       "  'learning_rate': 4.864998353638459e-05,\n",
       "  'epoch': 0.37325038880248834,\n",
       "  'step': 420},\n",
       " {'loss': 5.5603,\n",
       "  'grad_norm': 1.0021259784698486,\n",
       "  'learning_rate': 4.848534738228515e-05,\n",
       "  'epoch': 0.3821373028215952,\n",
       "  'step': 430},\n",
       " {'loss': 5.5611,\n",
       "  'grad_norm': 1.0153981447219849,\n",
       "  'learning_rate': 4.832071122818571e-05,\n",
       "  'epoch': 0.39102421684070204,\n",
       "  'step': 440},\n",
       " {'loss': 5.5451,\n",
       "  'grad_norm': 1.0295703411102295,\n",
       "  'learning_rate': 4.815607507408627e-05,\n",
       "  'epoch': 0.39991113085980895,\n",
       "  'step': 450},\n",
       " {'loss': 5.5419,\n",
       "  'grad_norm': 1.0398471355438232,\n",
       "  'learning_rate': 4.799143891998683e-05,\n",
       "  'epoch': 0.4087980448789158,\n",
       "  'step': 460},\n",
       " {'loss': 5.5239,\n",
       "  'grad_norm': 1.0211623907089233,\n",
       "  'learning_rate': 4.7826802765887394e-05,\n",
       "  'epoch': 0.41768495889802265,\n",
       "  'step': 470},\n",
       " {'loss': 5.5251,\n",
       "  'grad_norm': 1.025394082069397,\n",
       "  'learning_rate': 4.766216661178795e-05,\n",
       "  'epoch': 0.4265718729171295,\n",
       "  'step': 480},\n",
       " {'loss': 5.5137,\n",
       "  'grad_norm': 1.0252705812454224,\n",
       "  'learning_rate': 4.749753045768851e-05,\n",
       "  'epoch': 0.4354587869362364,\n",
       "  'step': 490},\n",
       " {'loss': 5.4893,\n",
       "  'grad_norm': 1.1059173345565796,\n",
       "  'learning_rate': 4.733289430358907e-05,\n",
       "  'epoch': 0.44434570095534326,\n",
       "  'step': 500},\n",
       " {'loss': 5.4775,\n",
       "  'grad_norm': 1.0362131595611572,\n",
       "  'learning_rate': 4.716825814948963e-05,\n",
       "  'epoch': 0.4532326149744501,\n",
       "  'step': 510},\n",
       " {'loss': 5.455,\n",
       "  'grad_norm': 1.0863900184631348,\n",
       "  'learning_rate': 4.700362199539019e-05,\n",
       "  'epoch': 0.46211952899355696,\n",
       "  'step': 520},\n",
       " {'loss': 5.4574,\n",
       "  'grad_norm': 1.064353108406067,\n",
       "  'learning_rate': 4.683898584129075e-05,\n",
       "  'epoch': 0.47100644301266387,\n",
       "  'step': 530},\n",
       " {'loss': 5.4573,\n",
       "  'grad_norm': 1.0639770030975342,\n",
       "  'learning_rate': 4.667434968719131e-05,\n",
       "  'epoch': 0.4798933570317707,\n",
       "  'step': 540},\n",
       " {'loss': 5.4273,\n",
       "  'grad_norm': 1.0746970176696777,\n",
       "  'learning_rate': 4.650971353309187e-05,\n",
       "  'epoch': 0.48878027105087757,\n",
       "  'step': 550},\n",
       " {'loss': 5.4074,\n",
       "  'grad_norm': 1.0758410692214966,\n",
       "  'learning_rate': 4.634507737899243e-05,\n",
       "  'epoch': 0.4976671850699845,\n",
       "  'step': 560},\n",
       " {'loss': 5.4033,\n",
       "  'grad_norm': 1.0645757913589478,\n",
       "  'learning_rate': 4.618044122489299e-05,\n",
       "  'epoch': 0.5065540990890913,\n",
       "  'step': 570},\n",
       " {'loss': 5.3828,\n",
       "  'grad_norm': 1.191333293914795,\n",
       "  'learning_rate': 4.601580507079355e-05,\n",
       "  'epoch': 0.5154410131081982,\n",
       "  'step': 580},\n",
       " {'loss': 5.3835,\n",
       "  'grad_norm': 1.102504849433899,\n",
       "  'learning_rate': 4.585116891669411e-05,\n",
       "  'epoch': 0.524327927127305,\n",
       "  'step': 590},\n",
       " {'loss': 5.3557,\n",
       "  'grad_norm': 1.171553134918213,\n",
       "  'learning_rate': 4.568653276259467e-05,\n",
       "  'epoch': 0.5332148411464119,\n",
       "  'step': 600},\n",
       " {'loss': 5.3869,\n",
       "  'grad_norm': 1.14118230342865,\n",
       "  'learning_rate': 4.5521896608495226e-05,\n",
       "  'epoch': 0.5421017551655187,\n",
       "  'step': 610},\n",
       " {'loss': 5.3617,\n",
       "  'grad_norm': 1.0469740629196167,\n",
       "  'learning_rate': 4.535726045439579e-05,\n",
       "  'epoch': 0.5509886691846256,\n",
       "  'step': 620},\n",
       " {'loss': 5.3716,\n",
       "  'grad_norm': 1.0866329669952393,\n",
       "  'learning_rate': 4.519262430029635e-05,\n",
       "  'epoch': 0.5598755832037325,\n",
       "  'step': 630},\n",
       " {'loss': 5.3579,\n",
       "  'grad_norm': 1.26162850856781,\n",
       "  'learning_rate': 4.502798814619691e-05,\n",
       "  'epoch': 0.5687624972228393,\n",
       "  'step': 640},\n",
       " {'loss': 5.3452,\n",
       "  'grad_norm': 1.2066603899002075,\n",
       "  'learning_rate': 4.486335199209747e-05,\n",
       "  'epoch': 0.5776494112419462,\n",
       "  'step': 650},\n",
       " {'loss': 5.338,\n",
       "  'grad_norm': 1.1669186353683472,\n",
       "  'learning_rate': 4.469871583799803e-05,\n",
       "  'epoch': 0.5865363252610531,\n",
       "  'step': 660},\n",
       " {'loss': 5.3395,\n",
       "  'grad_norm': 1.163892388343811,\n",
       "  'learning_rate': 4.453407968389859e-05,\n",
       "  'epoch': 0.5954232392801599,\n",
       "  'step': 670},\n",
       " {'loss': 5.2991,\n",
       "  'grad_norm': 1.1629446744918823,\n",
       "  'learning_rate': 4.4369443529799146e-05,\n",
       "  'epoch': 0.6043101532992669,\n",
       "  'step': 680},\n",
       " {'loss': 5.2784,\n",
       "  'grad_norm': 1.2173254489898682,\n",
       "  'learning_rate': 4.420480737569971e-05,\n",
       "  'epoch': 0.6131970673183736,\n",
       "  'step': 690},\n",
       " {'loss': 5.2906,\n",
       "  'grad_norm': 1.1684532165527344,\n",
       "  'learning_rate': 4.404017122160027e-05,\n",
       "  'epoch': 0.6220839813374806,\n",
       "  'step': 700},\n",
       " {'loss': 5.3025,\n",
       "  'grad_norm': 1.1199374198913574,\n",
       "  'learning_rate': 4.387553506750082e-05,\n",
       "  'epoch': 0.6309708953565875,\n",
       "  'step': 710},\n",
       " {'loss': 5.2774,\n",
       "  'grad_norm': 1.1252669095993042,\n",
       "  'learning_rate': 4.3710898913401383e-05,\n",
       "  'epoch': 0.6398578093756943,\n",
       "  'step': 720},\n",
       " {'loss': 5.288,\n",
       "  'grad_norm': 1.135992169380188,\n",
       "  'learning_rate': 4.354626275930194e-05,\n",
       "  'epoch': 0.6487447233948012,\n",
       "  'step': 730},\n",
       " {'loss': 5.2423,\n",
       "  'grad_norm': 1.2881770133972168,\n",
       "  'learning_rate': 4.33816266052025e-05,\n",
       "  'epoch': 0.6576316374139081,\n",
       "  'step': 740},\n",
       " {'loss': 5.2597,\n",
       "  'grad_norm': 1.199745535850525,\n",
       "  'learning_rate': 4.3216990451103065e-05,\n",
       "  'epoch': 0.6665185514330149,\n",
       "  'step': 750},\n",
       " {'loss': 5.2415,\n",
       "  'grad_norm': 1.1572469472885132,\n",
       "  'learning_rate': 4.305235429700362e-05,\n",
       "  'epoch': 0.6754054654521218,\n",
       "  'step': 760},\n",
       " {'loss': 5.2718,\n",
       "  'grad_norm': 1.2711989879608154,\n",
       "  'learning_rate': 4.2887718142904184e-05,\n",
       "  'epoch': 0.6842923794712286,\n",
       "  'step': 770},\n",
       " {'loss': 5.2334,\n",
       "  'grad_norm': 1.1748969554901123,\n",
       "  'learning_rate': 4.272308198880474e-05,\n",
       "  'epoch': 0.6931792934903355,\n",
       "  'step': 780},\n",
       " {'loss': 5.2261,\n",
       "  'grad_norm': 1.2650811672210693,\n",
       "  'learning_rate': 4.25584458347053e-05,\n",
       "  'epoch': 0.7020662075094424,\n",
       "  'step': 790},\n",
       " {'loss': 5.1913,\n",
       "  'grad_norm': 1.1326992511749268,\n",
       "  'learning_rate': 4.239380968060586e-05,\n",
       "  'epoch': 0.7109531215285492,\n",
       "  'step': 800},\n",
       " {'loss': 5.2204,\n",
       "  'grad_norm': 1.2859127521514893,\n",
       "  'learning_rate': 4.222917352650642e-05,\n",
       "  'epoch': 0.7198400355476561,\n",
       "  'step': 810},\n",
       " {'loss': 5.1644,\n",
       "  'grad_norm': 1.2114582061767578,\n",
       "  'learning_rate': 4.206453737240698e-05,\n",
       "  'epoch': 0.728726949566763,\n",
       "  'step': 820},\n",
       " {'loss': 5.1907,\n",
       "  'grad_norm': 1.3865121603012085,\n",
       "  'learning_rate': 4.189990121830754e-05,\n",
       "  'epoch': 0.7376138635858698,\n",
       "  'step': 830},\n",
       " {'loss': 5.1782,\n",
       "  'grad_norm': 1.4485503435134888,\n",
       "  'learning_rate': 4.1735265064208104e-05,\n",
       "  'epoch': 0.7465007776049767,\n",
       "  'step': 840},\n",
       " {'loss': 5.1721,\n",
       "  'grad_norm': 1.2274363040924072,\n",
       "  'learning_rate': 4.157062891010866e-05,\n",
       "  'epoch': 0.7553876916240836,\n",
       "  'step': 850},\n",
       " {'loss': 5.1578,\n",
       "  'grad_norm': 1.1913232803344727,\n",
       "  'learning_rate': 4.140599275600922e-05,\n",
       "  'epoch': 0.7642746056431904,\n",
       "  'step': 860},\n",
       " {'loss': 5.1588,\n",
       "  'grad_norm': 1.1764495372772217,\n",
       "  'learning_rate': 4.124135660190978e-05,\n",
       "  'epoch': 0.7731615196622973,\n",
       "  'step': 870},\n",
       " {'loss': 5.138,\n",
       "  'grad_norm': 1.2760459184646606,\n",
       "  'learning_rate': 4.107672044781034e-05,\n",
       "  'epoch': 0.7820484336814041,\n",
       "  'step': 880},\n",
       " {'loss': 5.1416,\n",
       "  'grad_norm': 1.248575210571289,\n",
       "  'learning_rate': 4.09120842937109e-05,\n",
       "  'epoch': 0.790935347700511,\n",
       "  'step': 890},\n",
       " {'loss': 5.0944,\n",
       "  'grad_norm': 1.395334243774414,\n",
       "  'learning_rate': 4.074744813961146e-05,\n",
       "  'epoch': 0.7998222617196179,\n",
       "  'step': 900},\n",
       " {'loss': 5.1249,\n",
       "  'grad_norm': 1.2850102186203003,\n",
       "  'learning_rate': 4.058281198551202e-05,\n",
       "  'epoch': 0.8087091757387247,\n",
       "  'step': 910},\n",
       " {'loss': 5.0738,\n",
       "  'grad_norm': 1.473629117012024,\n",
       "  'learning_rate': 4.041817583141258e-05,\n",
       "  'epoch': 0.8175960897578316,\n",
       "  'step': 920},\n",
       " {'loss': 5.1401,\n",
       "  'grad_norm': 1.442339301109314,\n",
       "  'learning_rate': 4.025353967731314e-05,\n",
       "  'epoch': 0.8264830037769385,\n",
       "  'step': 930},\n",
       " {'loss': 5.1242,\n",
       "  'grad_norm': 1.52792227268219,\n",
       "  'learning_rate': 4.00889035232137e-05,\n",
       "  'epoch': 0.8353699177960453,\n",
       "  'step': 940},\n",
       " {'loss': 5.0916,\n",
       "  'grad_norm': 1.3267822265625,\n",
       "  'learning_rate': 3.992426736911426e-05,\n",
       "  'epoch': 0.8442568318151522,\n",
       "  'step': 950},\n",
       " {'loss': 5.0493,\n",
       "  'grad_norm': 1.338683009147644,\n",
       "  'learning_rate': 3.975963121501482e-05,\n",
       "  'epoch': 0.853143745834259,\n",
       "  'step': 960},\n",
       " {'loss': 5.0832,\n",
       "  'grad_norm': 1.54911208152771,\n",
       "  'learning_rate': 3.959499506091538e-05,\n",
       "  'epoch': 0.8620306598533659,\n",
       "  'step': 970},\n",
       " {'loss': 5.0989,\n",
       "  'grad_norm': 1.473403811454773,\n",
       "  'learning_rate': 3.943035890681594e-05,\n",
       "  'epoch': 0.8709175738724728,\n",
       "  'step': 980},\n",
       " {'loss': 5.0604,\n",
       "  'grad_norm': 1.350206732749939,\n",
       "  'learning_rate': 3.92657227527165e-05,\n",
       "  'epoch': 0.8798044878915796,\n",
       "  'step': 990},\n",
       " {'loss': 5.0073,\n",
       "  'grad_norm': 1.30962073802948,\n",
       "  'learning_rate': 3.910108659861706e-05,\n",
       "  'epoch': 0.8886914019106865,\n",
       "  'step': 1000},\n",
       " {'loss': 5.033,\n",
       "  'grad_norm': 1.6430158615112305,\n",
       "  'learning_rate': 3.893645044451762e-05,\n",
       "  'epoch': 0.8975783159297934,\n",
       "  'step': 1010},\n",
       " {'loss': 5.0126,\n",
       "  'grad_norm': 1.2798100709915161,\n",
       "  'learning_rate': 3.877181429041818e-05,\n",
       "  'epoch': 0.9064652299489002,\n",
       "  'step': 1020},\n",
       " {'loss': 5.0383,\n",
       "  'grad_norm': 1.3781015872955322,\n",
       "  'learning_rate': 3.8607178136318737e-05,\n",
       "  'epoch': 0.9153521439680071,\n",
       "  'step': 1030},\n",
       " {'loss': 5.0614,\n",
       "  'grad_norm': 1.4126278162002563,\n",
       "  'learning_rate': 3.84425419822193e-05,\n",
       "  'epoch': 0.9242390579871139,\n",
       "  'step': 1040},\n",
       " {'loss': 4.9514,\n",
       "  'grad_norm': 1.5056147575378418,\n",
       "  'learning_rate': 3.8277905828119855e-05,\n",
       "  'epoch': 0.9331259720062208,\n",
       "  'step': 1050},\n",
       " {'loss': 5.0044,\n",
       "  'grad_norm': 1.5471025705337524,\n",
       "  'learning_rate': 3.811326967402042e-05,\n",
       "  'epoch': 0.9420128860253277,\n",
       "  'step': 1060},\n",
       " {'loss': 4.9782,\n",
       "  'grad_norm': 1.3459399938583374,\n",
       "  'learning_rate': 3.794863351992098e-05,\n",
       "  'epoch': 0.9508998000444345,\n",
       "  'step': 1070},\n",
       " {'loss': 5.0109,\n",
       "  'grad_norm': 1.3685293197631836,\n",
       "  'learning_rate': 3.778399736582154e-05,\n",
       "  'epoch': 0.9597867140635414,\n",
       "  'step': 1080},\n",
       " {'loss': 4.9736,\n",
       "  'grad_norm': 1.3539719581604004,\n",
       "  'learning_rate': 3.76193612117221e-05,\n",
       "  'epoch': 0.9686736280826483,\n",
       "  'step': 1090},\n",
       " {'loss': 5.0261,\n",
       "  'grad_norm': 1.5707237720489502,\n",
       "  'learning_rate': 3.7454725057622656e-05,\n",
       "  'epoch': 0.9775605421017551,\n",
       "  'step': 1100},\n",
       " {'loss': 5.0025,\n",
       "  'grad_norm': 1.4463677406311035,\n",
       "  'learning_rate': 3.729008890352322e-05,\n",
       "  'epoch': 0.986447456120862,\n",
       "  'step': 1110},\n",
       " {'loss': 5.0535,\n",
       "  'grad_norm': 1.4711565971374512,\n",
       "  'learning_rate': 3.7125452749423775e-05,\n",
       "  'epoch': 0.995334370139969,\n",
       "  'step': 1120},\n",
       " {'eval_loss': 4.957149982452393,\n",
       "  'eval_accuracy': 0.2169728783902012,\n",
       "  'eval_runtime': 76.0873,\n",
       "  'eval_samples_per_second': 105.156,\n",
       "  'eval_steps_per_second': 13.156,\n",
       "  'epoch': 0.9997778271495223,\n",
       "  'step': 1125},\n",
       " {'loss': 4.8993,\n",
       "  'grad_norm': 1.3653733730316162,\n",
       "  'learning_rate': 3.696081659532433e-05,\n",
       "  'epoch': 1.0042212841590759,\n",
       "  'step': 1130},\n",
       " {'loss': 4.8834,\n",
       "  'grad_norm': 1.4774038791656494,\n",
       "  'learning_rate': 3.6796180441224894e-05,\n",
       "  'epoch': 1.0131081981781827,\n",
       "  'step': 1140},\n",
       " {'loss': 4.9007,\n",
       "  'grad_norm': 1.4379853010177612,\n",
       "  'learning_rate': 3.663154428712545e-05,\n",
       "  'epoch': 1.0219951121972894,\n",
       "  'step': 1150},\n",
       " {'loss': 4.8714,\n",
       "  'grad_norm': 1.4435831308364868,\n",
       "  'learning_rate': 3.646690813302601e-05,\n",
       "  'epoch': 1.0308820262163965,\n",
       "  'step': 1160},\n",
       " {'loss': 4.9021,\n",
       "  'grad_norm': 1.6181260347366333,\n",
       "  'learning_rate': 3.630227197892657e-05,\n",
       "  'epoch': 1.0397689402355033,\n",
       "  'step': 1170},\n",
       " {'loss': 4.9192,\n",
       "  'grad_norm': 1.494800090789795,\n",
       "  'learning_rate': 3.613763582482713e-05,\n",
       "  'epoch': 1.04865585425461,\n",
       "  'step': 1180},\n",
       " {'loss': 4.8974,\n",
       "  'grad_norm': 2.6939356327056885,\n",
       "  'learning_rate': 3.5972999670727694e-05,\n",
       "  'epoch': 1.0575427682737168,\n",
       "  'step': 1190},\n",
       " {'loss': 4.963,\n",
       "  'grad_norm': 1.4140138626098633,\n",
       "  'learning_rate': 3.580836351662825e-05,\n",
       "  'epoch': 1.0664296822928239,\n",
       "  'step': 1200},\n",
       " {'loss': 4.8773,\n",
       "  'grad_norm': 1.3485630750656128,\n",
       "  'learning_rate': 3.564372736252881e-05,\n",
       "  'epoch': 1.0753165963119307,\n",
       "  'step': 1210},\n",
       " {'loss': 4.8696,\n",
       "  'grad_norm': 1.4951151609420776,\n",
       "  'learning_rate': 3.547909120842937e-05,\n",
       "  'epoch': 1.0842035103310375,\n",
       "  'step': 1220},\n",
       " {'loss': 4.8978,\n",
       "  'grad_norm': 1.7047938108444214,\n",
       "  'learning_rate': 3.531445505432993e-05,\n",
       "  'epoch': 1.0930904243501445,\n",
       "  'step': 1230},\n",
       " {'loss': 4.8382,\n",
       "  'grad_norm': 1.6638559103012085,\n",
       "  'learning_rate': 3.514981890023049e-05,\n",
       "  'epoch': 1.1019773383692513,\n",
       "  'step': 1240},\n",
       " {'loss': 4.8815,\n",
       "  'grad_norm': 1.5207560062408447,\n",
       "  'learning_rate': 3.498518274613105e-05,\n",
       "  'epoch': 1.110864252388358,\n",
       "  'step': 1250},\n",
       " {'loss': 4.8556,\n",
       "  'grad_norm': 1.35409414768219,\n",
       "  'learning_rate': 3.4820546592031614e-05,\n",
       "  'epoch': 1.119751166407465,\n",
       "  'step': 1260},\n",
       " {'loss': 4.803,\n",
       "  'grad_norm': 1.5662429332733154,\n",
       "  'learning_rate': 3.465591043793217e-05,\n",
       "  'epoch': 1.1286380804265719,\n",
       "  'step': 1270},\n",
       " {'loss': 4.7951,\n",
       "  'grad_norm': 1.662122368812561,\n",
       "  'learning_rate': 3.449127428383273e-05,\n",
       "  'epoch': 1.1375249944456787,\n",
       "  'step': 1280},\n",
       " {'loss': 4.8152,\n",
       "  'grad_norm': 1.4856303930282593,\n",
       "  'learning_rate': 3.432663812973329e-05,\n",
       "  'epoch': 1.1464119084647857,\n",
       "  'step': 1290},\n",
       " {'loss': 4.7962,\n",
       "  'grad_norm': 1.6326074600219727,\n",
       "  'learning_rate': 3.416200197563385e-05,\n",
       "  'epoch': 1.1552988224838925,\n",
       "  'step': 1300},\n",
       " {'loss': 4.8564,\n",
       "  'grad_norm': 1.6111441850662231,\n",
       "  'learning_rate': 3.399736582153441e-05,\n",
       "  'epoch': 1.1641857365029993,\n",
       "  'step': 1310},\n",
       " {'loss': 4.747,\n",
       "  'grad_norm': 1.5864267349243164,\n",
       "  'learning_rate': 3.383272966743497e-05,\n",
       "  'epoch': 1.1730726505221063,\n",
       "  'step': 1320},\n",
       " {'loss': 4.8076,\n",
       "  'grad_norm': 1.5208265781402588,\n",
       "  'learning_rate': 3.3668093513335534e-05,\n",
       "  'epoch': 1.181959564541213,\n",
       "  'step': 1330},\n",
       " {'loss': 4.8095,\n",
       "  'grad_norm': 1.5489100217819214,\n",
       "  'learning_rate': 3.350345735923609e-05,\n",
       "  'epoch': 1.1908464785603199,\n",
       "  'step': 1340},\n",
       " {'loss': 4.7641,\n",
       "  'grad_norm': 1.8204108476638794,\n",
       "  'learning_rate': 3.333882120513665e-05,\n",
       "  'epoch': 1.199733392579427,\n",
       "  'step': 1350},\n",
       " {'loss': 4.7658,\n",
       "  'grad_norm': 1.545945167541504,\n",
       "  'learning_rate': 3.317418505103721e-05,\n",
       "  'epoch': 1.2086203065985337,\n",
       "  'step': 1360},\n",
       " {'loss': 4.8105,\n",
       "  'grad_norm': 1.6714030504226685,\n",
       "  'learning_rate': 3.300954889693777e-05,\n",
       "  'epoch': 1.2175072206176405,\n",
       "  'step': 1370},\n",
       " {'loss': 4.7846,\n",
       "  'grad_norm': 1.8168423175811768,\n",
       "  'learning_rate': 3.284491274283833e-05,\n",
       "  'epoch': 1.2263941346367475,\n",
       "  'step': 1380},\n",
       " {'loss': 4.7641,\n",
       "  'grad_norm': 1.8509114980697632,\n",
       "  'learning_rate': 3.268027658873889e-05,\n",
       "  'epoch': 1.2352810486558543,\n",
       "  'step': 1390},\n",
       " {'loss': 4.7906,\n",
       "  'grad_norm': 1.681134581565857,\n",
       "  'learning_rate': 3.2515640434639446e-05,\n",
       "  'epoch': 1.244167962674961,\n",
       "  'step': 1400},\n",
       " {'loss': 4.7975,\n",
       "  'grad_norm': 1.5602340698242188,\n",
       "  'learning_rate': 3.235100428054001e-05,\n",
       "  'epoch': 1.2530548766940681,\n",
       "  'step': 1410},\n",
       " {'loss': 4.7821,\n",
       "  'grad_norm': 1.623347520828247,\n",
       "  'learning_rate': 3.218636812644057e-05,\n",
       "  'epoch': 1.261941790713175,\n",
       "  'step': 1420},\n",
       " {'loss': 4.7265,\n",
       "  'grad_norm': 1.9641783237457275,\n",
       "  'learning_rate': 3.202173197234113e-05,\n",
       "  'epoch': 1.2708287047322817,\n",
       "  'step': 1430},\n",
       " {'loss': 4.7221,\n",
       "  'grad_norm': 1.962636113166809,\n",
       "  'learning_rate': 3.185709581824169e-05,\n",
       "  'epoch': 1.2797156187513885,\n",
       "  'step': 1440},\n",
       " {'loss': 4.7399,\n",
       "  'grad_norm': 1.9231404066085815,\n",
       "  'learning_rate': 3.169245966414225e-05,\n",
       "  'epoch': 1.2886025327704955,\n",
       "  'step': 1450},\n",
       " {'loss': 4.707,\n",
       "  'grad_norm': 1.408921718597412,\n",
       "  'learning_rate': 3.152782351004281e-05,\n",
       "  'epoch': 1.2974894467896023,\n",
       "  'step': 1460},\n",
       " {'loss': 4.764,\n",
       "  'grad_norm': 1.4177690744400024,\n",
       "  'learning_rate': 3.1363187355943366e-05,\n",
       "  'epoch': 1.3063763608087091,\n",
       "  'step': 1470},\n",
       " {'loss': 4.7577,\n",
       "  'grad_norm': 1.5957190990447998,\n",
       "  'learning_rate': 3.119855120184393e-05,\n",
       "  'epoch': 1.315263274827816,\n",
       "  'step': 1480},\n",
       " {'loss': 4.667,\n",
       "  'grad_norm': 1.6137858629226685,\n",
       "  'learning_rate': 3.103391504774449e-05,\n",
       "  'epoch': 1.324150188846923,\n",
       "  'step': 1490},\n",
       " {'loss': 4.7084,\n",
       "  'grad_norm': 2.0900332927703857,\n",
       "  'learning_rate': 3.086927889364505e-05,\n",
       "  'epoch': 1.3330371028660297,\n",
       "  'step': 1500},\n",
       " {'loss': 4.6616,\n",
       "  'grad_norm': 1.6454256772994995,\n",
       "  'learning_rate': 3.070464273954561e-05,\n",
       "  'epoch': 1.3419240168851365,\n",
       "  'step': 1510},\n",
       " {'loss': 4.6672,\n",
       "  'grad_norm': 1.6279855966567993,\n",
       "  'learning_rate': 3.0540006585446166e-05,\n",
       "  'epoch': 1.3508109309042435,\n",
       "  'step': 1520},\n",
       " {'loss': 4.7197,\n",
       "  'grad_norm': 1.7160922288894653,\n",
       "  'learning_rate': 3.0375370431346723e-05,\n",
       "  'epoch': 1.3596978449233503,\n",
       "  'step': 1530},\n",
       " {'loss': 4.7086,\n",
       "  'grad_norm': 1.8108189105987549,\n",
       "  'learning_rate': 3.0210734277247282e-05,\n",
       "  'epoch': 1.3685847589424571,\n",
       "  'step': 1540},\n",
       " {'loss': 4.6895,\n",
       "  'grad_norm': 1.685046672821045,\n",
       "  'learning_rate': 3.004609812314784e-05,\n",
       "  'epoch': 1.3774716729615641,\n",
       "  'step': 1550},\n",
       " {'loss': 4.6985,\n",
       "  'grad_norm': 1.6361486911773682,\n",
       "  'learning_rate': 2.98814619690484e-05,\n",
       "  'epoch': 1.386358586980671,\n",
       "  'step': 1560},\n",
       " {'loss': 4.639,\n",
       "  'grad_norm': 1.6749613285064697,\n",
       "  'learning_rate': 2.9716825814948964e-05,\n",
       "  'epoch': 1.3952455009997777,\n",
       "  'step': 1570},\n",
       " {'loss': 4.7094,\n",
       "  'grad_norm': 1.5922151803970337,\n",
       "  'learning_rate': 2.9552189660849523e-05,\n",
       "  'epoch': 1.4041324150188847,\n",
       "  'step': 1580},\n",
       " {'loss': 4.7153,\n",
       "  'grad_norm': 1.5665174722671509,\n",
       "  'learning_rate': 2.9387553506750083e-05,\n",
       "  'epoch': 1.4130193290379915,\n",
       "  'step': 1590},\n",
       " {'loss': 4.6414,\n",
       "  'grad_norm': 2.0264580249786377,\n",
       "  'learning_rate': 2.9222917352650642e-05,\n",
       "  'epoch': 1.4219062430570983,\n",
       "  'step': 1600},\n",
       " {'loss': 4.7188,\n",
       "  'grad_norm': 1.818171739578247,\n",
       "  'learning_rate': 2.90582811985512e-05,\n",
       "  'epoch': 1.4307931570762054,\n",
       "  'step': 1610},\n",
       " {'loss': 4.6517,\n",
       "  'grad_norm': 1.7088600397109985,\n",
       "  'learning_rate': 2.889364504445176e-05,\n",
       "  'epoch': 1.4396800710953122,\n",
       "  'step': 1620},\n",
       " {'loss': 4.6057,\n",
       "  'grad_norm': 2.086190938949585,\n",
       "  'learning_rate': 2.872900889035232e-05,\n",
       "  'epoch': 1.448566985114419,\n",
       "  'step': 1630},\n",
       " {'loss': 4.5875,\n",
       "  'grad_norm': 1.6953229904174805,\n",
       "  'learning_rate': 2.856437273625288e-05,\n",
       "  'epoch': 1.457453899133526,\n",
       "  'step': 1640},\n",
       " {'loss': 4.6801,\n",
       "  'grad_norm': 1.7572556734085083,\n",
       "  'learning_rate': 2.8399736582153443e-05,\n",
       "  'epoch': 1.4663408131526328,\n",
       "  'step': 1650},\n",
       " {'loss': 4.6358,\n",
       "  'grad_norm': 1.6181272268295288,\n",
       "  'learning_rate': 2.8235100428054002e-05,\n",
       "  'epoch': 1.4752277271717396,\n",
       "  'step': 1660},\n",
       " {'loss': 4.5666,\n",
       "  'grad_norm': 1.9466805458068848,\n",
       "  'learning_rate': 2.807046427395456e-05,\n",
       "  'epoch': 1.4841146411908466,\n",
       "  'step': 1670},\n",
       " {'loss': 4.6613,\n",
       "  'grad_norm': 2.4543793201446533,\n",
       "  'learning_rate': 2.790582811985512e-05,\n",
       "  'epoch': 1.4930015552099534,\n",
       "  'step': 1680},\n",
       " {'loss': 4.6178,\n",
       "  'grad_norm': 1.6554107666015625,\n",
       "  'learning_rate': 2.774119196575568e-05,\n",
       "  'epoch': 1.5018884692290602,\n",
       "  'step': 1690},\n",
       " {'loss': 4.6576,\n",
       "  'grad_norm': 1.7807337045669556,\n",
       "  'learning_rate': 2.757655581165624e-05,\n",
       "  'epoch': 1.5107753832481672,\n",
       "  'step': 1700},\n",
       " {'loss': 4.6348,\n",
       "  'grad_norm': 1.6393810510635376,\n",
       "  'learning_rate': 2.74119196575568e-05,\n",
       "  'epoch': 1.519662297267274,\n",
       "  'step': 1710},\n",
       " {'loss': 4.6087,\n",
       "  'grad_norm': 1.5589855909347534,\n",
       "  'learning_rate': 2.724728350345736e-05,\n",
       "  'epoch': 1.5285492112863808,\n",
       "  'step': 1720},\n",
       " {'loss': 4.5861,\n",
       "  'grad_norm': 1.9289544820785522,\n",
       "  'learning_rate': 2.708264734935792e-05,\n",
       "  'epoch': 1.5374361253054878,\n",
       "  'step': 1730},\n",
       " {'loss': 4.6183,\n",
       "  'grad_norm': 2.1846375465393066,\n",
       "  'learning_rate': 2.691801119525848e-05,\n",
       "  'epoch': 1.5463230393245946,\n",
       "  'step': 1740},\n",
       " {'loss': 4.5402,\n",
       "  'grad_norm': 1.8190040588378906,\n",
       "  'learning_rate': 2.675337504115904e-05,\n",
       "  'epoch': 1.5552099533437014,\n",
       "  'step': 1750},\n",
       " {'loss': 4.5562,\n",
       "  'grad_norm': 1.6326746940612793,\n",
       "  'learning_rate': 2.65887388870596e-05,\n",
       "  'epoch': 1.5640968673628084,\n",
       "  'step': 1760},\n",
       " {'loss': 4.5397,\n",
       "  'grad_norm': 1.879045844078064,\n",
       "  'learning_rate': 2.642410273296016e-05,\n",
       "  'epoch': 1.572983781381915,\n",
       "  'step': 1770},\n",
       " {'loss': 4.5226,\n",
       "  'grad_norm': 1.8459217548370361,\n",
       "  'learning_rate': 2.625946657886072e-05,\n",
       "  'epoch': 1.581870695401022,\n",
       "  'step': 1780},\n",
       " {'loss': 4.593,\n",
       "  'grad_norm': 1.8687827587127686,\n",
       "  'learning_rate': 2.609483042476128e-05,\n",
       "  'epoch': 1.590757609420129,\n",
       "  'step': 1790},\n",
       " {'loss': 4.4887,\n",
       "  'grad_norm': 1.9361088275909424,\n",
       "  'learning_rate': 2.593019427066184e-05,\n",
       "  'epoch': 1.5996445234392356,\n",
       "  'step': 1800},\n",
       " {'loss': 4.6012,\n",
       "  'grad_norm': 2.0347678661346436,\n",
       "  'learning_rate': 2.57655581165624e-05,\n",
       "  'epoch': 1.6085314374583426,\n",
       "  'step': 1810},\n",
       " {'loss': 4.571,\n",
       "  'grad_norm': 1.723789930343628,\n",
       "  'learning_rate': 2.560092196246296e-05,\n",
       "  'epoch': 1.6174183514774496,\n",
       "  'step': 1820},\n",
       " {'loss': 4.5787,\n",
       "  'grad_norm': 1.8236076831817627,\n",
       "  'learning_rate': 2.543628580836352e-05,\n",
       "  'epoch': 1.6263052654965562,\n",
       "  'step': 1830},\n",
       " {'loss': 4.5485,\n",
       "  'grad_norm': 1.8603923320770264,\n",
       "  'learning_rate': 2.527164965426408e-05,\n",
       "  'epoch': 1.6351921795156632,\n",
       "  'step': 1840},\n",
       " {'loss': 4.5317,\n",
       "  'grad_norm': 1.928012728691101,\n",
       "  'learning_rate': 2.510701350016464e-05,\n",
       "  'epoch': 1.64407909353477,\n",
       "  'step': 1850},\n",
       " {'loss': 4.5481,\n",
       "  'grad_norm': 2.1910271644592285,\n",
       "  'learning_rate': 2.4942377346065194e-05,\n",
       "  'epoch': 1.6529660075538768,\n",
       "  'step': 1860},\n",
       " {'loss': 4.5798,\n",
       "  'grad_norm': 1.6672786474227905,\n",
       "  'learning_rate': 2.4777741191965757e-05,\n",
       "  'epoch': 1.6618529215729838,\n",
       "  'step': 1870},\n",
       " {'loss': 4.5436,\n",
       "  'grad_norm': 2.0598819255828857,\n",
       "  'learning_rate': 2.4613105037866317e-05,\n",
       "  'epoch': 1.6707398355920906,\n",
       "  'step': 1880},\n",
       " {'loss': 4.4461,\n",
       "  'grad_norm': 1.8676042556762695,\n",
       "  'learning_rate': 2.4448468883766876e-05,\n",
       "  'epoch': 1.6796267496111974,\n",
       "  'step': 1890},\n",
       " {'loss': 4.5279,\n",
       "  'grad_norm': 2.3574001789093018,\n",
       "  'learning_rate': 2.4283832729667436e-05,\n",
       "  'epoch': 1.6885136636303044,\n",
       "  'step': 1900},\n",
       " {'loss': 4.4389,\n",
       "  'grad_norm': 2.130352735519409,\n",
       "  'learning_rate': 2.4119196575567995e-05,\n",
       "  'epoch': 1.6974005776494112,\n",
       "  'step': 1910},\n",
       " {'loss': 4.4524,\n",
       "  'grad_norm': 2.0052897930145264,\n",
       "  'learning_rate': 2.3954560421468555e-05,\n",
       "  'epoch': 1.706287491668518,\n",
       "  'step': 1920},\n",
       " {'loss': 4.5323,\n",
       "  'grad_norm': 1.7837886810302734,\n",
       "  'learning_rate': 2.3789924267369114e-05,\n",
       "  'epoch': 1.715174405687625,\n",
       "  'step': 1930},\n",
       " {'loss': 4.553,\n",
       "  'grad_norm': 1.9908708333969116,\n",
       "  'learning_rate': 2.3625288113269677e-05,\n",
       "  'epoch': 1.7240613197067318,\n",
       "  'step': 1940},\n",
       " {'loss': 4.4689,\n",
       "  'grad_norm': 1.7523146867752075,\n",
       "  'learning_rate': 2.3460651959170236e-05,\n",
       "  'epoch': 1.7329482337258386,\n",
       "  'step': 1950},\n",
       " {'loss': 4.4961,\n",
       "  'grad_norm': 1.7291550636291504,\n",
       "  'learning_rate': 2.3296015805070796e-05,\n",
       "  'epoch': 1.7418351477449456,\n",
       "  'step': 1960},\n",
       " {'loss': 4.4888,\n",
       "  'grad_norm': 2.1533052921295166,\n",
       "  'learning_rate': 2.3131379650971355e-05,\n",
       "  'epoch': 1.7507220617640524,\n",
       "  'step': 1970},\n",
       " {'loss': 4.5131,\n",
       "  'grad_norm': 1.9543293714523315,\n",
       "  'learning_rate': 2.2966743496871915e-05,\n",
       "  'epoch': 1.7596089757831592,\n",
       "  'step': 1980},\n",
       " {'loss': 4.4538,\n",
       "  'grad_norm': 4.2156596183776855,\n",
       "  'learning_rate': 2.2802107342772474e-05,\n",
       "  'epoch': 1.7684958898022662,\n",
       "  'step': 1990},\n",
       " {'loss': 4.5049,\n",
       "  'grad_norm': 1.835220217704773,\n",
       "  'learning_rate': 2.2637471188673034e-05,\n",
       "  'epoch': 1.777382803821373,\n",
       "  'step': 2000},\n",
       " {'loss': 4.4284,\n",
       "  'grad_norm': 2.02307391166687,\n",
       "  'learning_rate': 2.2472835034573593e-05,\n",
       "  'epoch': 1.7862697178404798,\n",
       "  'step': 2010},\n",
       " {'loss': 4.4446,\n",
       "  'grad_norm': 1.7700648307800293,\n",
       "  'learning_rate': 2.2308198880474156e-05,\n",
       "  'epoch': 1.7951566318595868,\n",
       "  'step': 2020},\n",
       " {'loss': 4.5272,\n",
       "  'grad_norm': 1.6484451293945312,\n",
       "  'learning_rate': 2.2143562726374715e-05,\n",
       "  'epoch': 1.8040435458786936,\n",
       "  'step': 2030},\n",
       " {'loss': 4.4403,\n",
       "  'grad_norm': 1.8010808229446411,\n",
       "  'learning_rate': 2.1978926572275275e-05,\n",
       "  'epoch': 1.8129304598978004,\n",
       "  'step': 2040},\n",
       " {'loss': 4.4812,\n",
       "  'grad_norm': 2.0002143383026123,\n",
       "  'learning_rate': 2.181429041817583e-05,\n",
       "  'epoch': 1.8218173739169075,\n",
       "  'step': 2050},\n",
       " {'loss': 4.3571,\n",
       "  'grad_norm': 1.9937148094177246,\n",
       "  'learning_rate': 2.164965426407639e-05,\n",
       "  'epoch': 1.8307042879360143,\n",
       "  'step': 2060},\n",
       " {'loss': 4.4129,\n",
       "  'grad_norm': 2.1688485145568848,\n",
       "  'learning_rate': 2.148501810997695e-05,\n",
       "  'epoch': 1.839591201955121,\n",
       "  'step': 2070},\n",
       " {'loss': 4.4472,\n",
       "  'grad_norm': 2.5233073234558105,\n",
       "  'learning_rate': 2.1320381955877512e-05,\n",
       "  'epoch': 1.848478115974228,\n",
       "  'step': 2080},\n",
       " {'loss': 4.4606,\n",
       "  'grad_norm': 2.075115919113159,\n",
       "  'learning_rate': 2.1155745801778072e-05,\n",
       "  'epoch': 1.8573650299933349,\n",
       "  'step': 2090},\n",
       " {'loss': 4.3794,\n",
       "  'grad_norm': 1.9001195430755615,\n",
       "  'learning_rate': 2.099110964767863e-05,\n",
       "  'epoch': 1.8662519440124417,\n",
       "  'step': 2100},\n",
       " {'loss': 4.4662,\n",
       "  'grad_norm': 2.232886791229248,\n",
       "  'learning_rate': 2.082647349357919e-05,\n",
       "  'epoch': 1.8751388580315487,\n",
       "  'step': 2110},\n",
       " {'loss': 4.3773,\n",
       "  'grad_norm': 2.046858549118042,\n",
       "  'learning_rate': 2.066183733947975e-05,\n",
       "  'epoch': 1.8840257720506552,\n",
       "  'step': 2120},\n",
       " {'loss': 4.4018,\n",
       "  'grad_norm': 1.9070874452590942,\n",
       "  'learning_rate': 2.049720118538031e-05,\n",
       "  'epoch': 1.8929126860697623,\n",
       "  'step': 2130},\n",
       " {'loss': 4.462,\n",
       "  'grad_norm': 2.198991298675537,\n",
       "  'learning_rate': 2.033256503128087e-05,\n",
       "  'epoch': 1.9017996000888693,\n",
       "  'step': 2140},\n",
       " {'loss': 4.284,\n",
       "  'grad_norm': 2.391381025314331,\n",
       "  'learning_rate': 2.016792887718143e-05,\n",
       "  'epoch': 1.9106865141079759,\n",
       "  'step': 2150},\n",
       " {'loss': 4.4199,\n",
       "  'grad_norm': 2.237800121307373,\n",
       "  'learning_rate': 2.000329272308199e-05,\n",
       "  'epoch': 1.9195734281270829,\n",
       "  'step': 2160},\n",
       " {'loss': 4.4373,\n",
       "  'grad_norm': 2.134345531463623,\n",
       "  'learning_rate': 1.983865656898255e-05,\n",
       "  'epoch': 1.9284603421461899,\n",
       "  'step': 2170},\n",
       " {'loss': 4.3576,\n",
       "  'grad_norm': 2.0373694896698,\n",
       "  'learning_rate': 1.967402041488311e-05,\n",
       "  'epoch': 1.9373472561652965,\n",
       "  'step': 2180},\n",
       " {'loss': 4.4074,\n",
       "  'grad_norm': 2.308903932571411,\n",
       "  'learning_rate': 1.950938426078367e-05,\n",
       "  'epoch': 1.9462341701844035,\n",
       "  'step': 2190},\n",
       " {'loss': 4.3504,\n",
       "  'grad_norm': 1.865470290184021,\n",
       "  'learning_rate': 1.934474810668423e-05,\n",
       "  'epoch': 1.9551210842035105,\n",
       "  'step': 2200},\n",
       " {'loss': 4.3983,\n",
       "  'grad_norm': 1.9790452718734741,\n",
       "  'learning_rate': 1.918011195258479e-05,\n",
       "  'epoch': 1.964007998222617,\n",
       "  'step': 2210},\n",
       " {'loss': 4.3363,\n",
       "  'grad_norm': 1.9545035362243652,\n",
       "  'learning_rate': 1.9015475798485348e-05,\n",
       "  'epoch': 1.972894912241724,\n",
       "  'step': 2220},\n",
       " {'loss': 4.4049,\n",
       "  'grad_norm': 1.8681645393371582,\n",
       "  'learning_rate': 1.8850839644385908e-05,\n",
       "  'epoch': 1.9817818262608309,\n",
       "  'step': 2230},\n",
       " {'loss': 4.3726,\n",
       "  'grad_norm': 2.131404161453247,\n",
       "  'learning_rate': 1.868620349028647e-05,\n",
       "  'epoch': 1.9906687402799377,\n",
       "  'step': 2240},\n",
       " {'loss': 4.4188,\n",
       "  'grad_norm': 1.780598759651184,\n",
       "  'learning_rate': 1.8521567336187026e-05,\n",
       "  'epoch': 1.9995556542990447,\n",
       "  'step': 2250},\n",
       " {'eval_loss': 4.412487506866455,\n",
       "  'eval_accuracy': 0.2583427071616048,\n",
       "  'eval_runtime': 88.7081,\n",
       "  'eval_samples_per_second': 90.195,\n",
       "  'eval_steps_per_second': 11.284,\n",
       "  'epoch': 1.9995556542990447,\n",
       "  'step': 2250},\n",
       " {'loss': 4.2468,\n",
       "  'grad_norm': 1.8213180303573608,\n",
       "  'learning_rate': 1.8356931182087586e-05,\n",
       "  'epoch': 2.0084425683181517,\n",
       "  'step': 2260},\n",
       " {'loss': 4.3523,\n",
       "  'grad_norm': 2.8117318153381348,\n",
       "  'learning_rate': 1.8192295027988145e-05,\n",
       "  'epoch': 2.0173294823372583,\n",
       "  'step': 2270},\n",
       " {'loss': 4.3152,\n",
       "  'grad_norm': 1.7463642358779907,\n",
       "  'learning_rate': 1.8027658873888705e-05,\n",
       "  'epoch': 2.0262163963563653,\n",
       "  'step': 2280},\n",
       " {'loss': 4.3509,\n",
       "  'grad_norm': 1.9505913257598877,\n",
       "  'learning_rate': 1.7863022719789264e-05,\n",
       "  'epoch': 2.0351033103754723,\n",
       "  'step': 2290},\n",
       " {'loss': 4.2851,\n",
       "  'grad_norm': 2.282400369644165,\n",
       "  'learning_rate': 1.7698386565689827e-05,\n",
       "  'epoch': 2.043990224394579,\n",
       "  'step': 2300},\n",
       " {'loss': 4.2536,\n",
       "  'grad_norm': 2.147862195968628,\n",
       "  'learning_rate': 1.7533750411590387e-05,\n",
       "  'epoch': 2.052877138413686,\n",
       "  'step': 2310},\n",
       " {'loss': 4.3518,\n",
       "  'grad_norm': 2.477461099624634,\n",
       "  'learning_rate': 1.7369114257490946e-05,\n",
       "  'epoch': 2.061764052432793,\n",
       "  'step': 2320},\n",
       " {'loss': 4.3363,\n",
       "  'grad_norm': 1.6753101348876953,\n",
       "  'learning_rate': 1.7204478103391505e-05,\n",
       "  'epoch': 2.0706509664518995,\n",
       "  'step': 2330},\n",
       " {'loss': 4.3399,\n",
       "  'grad_norm': 1.9577041864395142,\n",
       "  'learning_rate': 1.7039841949292065e-05,\n",
       "  'epoch': 2.0795378804710065,\n",
       "  'step': 2340},\n",
       " {'loss': 4.3007,\n",
       "  'grad_norm': 2.1194701194763184,\n",
       "  'learning_rate': 1.6875205795192624e-05,\n",
       "  'epoch': 2.0884247944901135,\n",
       "  'step': 2350},\n",
       " {'loss': 4.2449,\n",
       "  'grad_norm': 1.779801845550537,\n",
       "  'learning_rate': 1.6710569641093184e-05,\n",
       "  'epoch': 2.09731170850922,\n",
       "  'step': 2360},\n",
       " {'loss': 4.2851,\n",
       "  'grad_norm': 2.6363956928253174,\n",
       "  'learning_rate': 1.6545933486993743e-05,\n",
       "  'epoch': 2.106198622528327,\n",
       "  'step': 2370},\n",
       " {'loss': 4.23,\n",
       "  'grad_norm': 2.7754321098327637,\n",
       "  'learning_rate': 1.6381297332894306e-05,\n",
       "  'epoch': 2.1150855365474337,\n",
       "  'step': 2380},\n",
       " {'loss': 4.221,\n",
       "  'grad_norm': 2.2485499382019043,\n",
       "  'learning_rate': 1.6216661178794866e-05,\n",
       "  'epoch': 2.1239724505665407,\n",
       "  'step': 2390},\n",
       " {'loss': 4.3667,\n",
       "  'grad_norm': 2.7162868976593018,\n",
       "  'learning_rate': 1.6052025024695425e-05,\n",
       "  'epoch': 2.1328593645856477,\n",
       "  'step': 2400},\n",
       " {'loss': 4.3149,\n",
       "  'grad_norm': 2.1848835945129395,\n",
       "  'learning_rate': 1.5887388870595984e-05,\n",
       "  'epoch': 2.1417462786047543,\n",
       "  'step': 2410},\n",
       " {'loss': 4.3353,\n",
       "  'grad_norm': 2.0698418617248535,\n",
       "  'learning_rate': 1.5722752716496544e-05,\n",
       "  'epoch': 2.1506331926238613,\n",
       "  'step': 2420},\n",
       " {'loss': 4.1865,\n",
       "  'grad_norm': 2.214423894882202,\n",
       "  'learning_rate': 1.5558116562397103e-05,\n",
       "  'epoch': 2.1595201066429683,\n",
       "  'step': 2430},\n",
       " {'loss': 4.3212,\n",
       "  'grad_norm': 2.677985668182373,\n",
       "  'learning_rate': 1.5393480408297663e-05,\n",
       "  'epoch': 2.168407020662075,\n",
       "  'step': 2440},\n",
       " {'loss': 4.3129,\n",
       "  'grad_norm': 2.016322374343872,\n",
       "  'learning_rate': 1.5228844254198224e-05,\n",
       "  'epoch': 2.177293934681182,\n",
       "  'step': 2450},\n",
       " {'loss': 4.3109,\n",
       "  'grad_norm': 1.9704530239105225,\n",
       "  'learning_rate': 1.5064208100098782e-05,\n",
       "  'epoch': 2.186180848700289,\n",
       "  'step': 2460},\n",
       " {'loss': 4.2537,\n",
       "  'grad_norm': 1.8943309783935547,\n",
       "  'learning_rate': 1.4899571945999341e-05,\n",
       "  'epoch': 2.1950677627193955,\n",
       "  'step': 2470},\n",
       " {'loss': 4.2847,\n",
       "  'grad_norm': 2.176987409591675,\n",
       "  'learning_rate': 1.47349357918999e-05,\n",
       "  'epoch': 2.2039546767385025,\n",
       "  'step': 2480},\n",
       " {'loss': 4.2501,\n",
       "  'grad_norm': 2.449345827102661,\n",
       "  'learning_rate': 1.457029963780046e-05,\n",
       "  'epoch': 2.2128415907576096,\n",
       "  'step': 2490},\n",
       " {'loss': 4.2694,\n",
       "  'grad_norm': 2.307283878326416,\n",
       "  'learning_rate': 1.4405663483701021e-05,\n",
       "  'epoch': 2.221728504776716,\n",
       "  'step': 2500},\n",
       " {'loss': 4.3147,\n",
       "  'grad_norm': 2.142667055130005,\n",
       "  'learning_rate': 1.424102732960158e-05,\n",
       "  'epoch': 2.230615418795823,\n",
       "  'step': 2510},\n",
       " {'loss': 4.2882,\n",
       "  'grad_norm': 2.0259907245635986,\n",
       "  'learning_rate': 1.407639117550214e-05,\n",
       "  'epoch': 2.23950233281493,\n",
       "  'step': 2520},\n",
       " {'loss': 4.2451,\n",
       "  'grad_norm': 2.038130283355713,\n",
       "  'learning_rate': 1.3911755021402701e-05,\n",
       "  'epoch': 2.2483892468340367,\n",
       "  'step': 2530},\n",
       " {'loss': 4.3035,\n",
       "  'grad_norm': 2.459458827972412,\n",
       "  'learning_rate': 1.374711886730326e-05,\n",
       "  'epoch': 2.2572761608531438,\n",
       "  'step': 2540},\n",
       " {'loss': 4.3372,\n",
       "  'grad_norm': 2.22371244430542,\n",
       "  'learning_rate': 1.358248271320382e-05,\n",
       "  'epoch': 2.2661630748722508,\n",
       "  'step': 2550},\n",
       " {'loss': 4.2165,\n",
       "  'grad_norm': 2.2588462829589844,\n",
       "  'learning_rate': 1.341784655910438e-05,\n",
       "  'epoch': 2.2750499888913573,\n",
       "  'step': 2560},\n",
       " {'loss': 4.2833,\n",
       "  'grad_norm': 2.9824864864349365,\n",
       "  'learning_rate': 1.325321040500494e-05,\n",
       "  'epoch': 2.2839369029104644,\n",
       "  'step': 2570},\n",
       " {'loss': 4.1877,\n",
       "  'grad_norm': 1.9008567333221436,\n",
       "  'learning_rate': 1.30885742509055e-05,\n",
       "  'epoch': 2.2928238169295714,\n",
       "  'step': 2580},\n",
       " {'loss': 4.2832,\n",
       "  'grad_norm': 2.397346258163452,\n",
       "  'learning_rate': 1.292393809680606e-05,\n",
       "  'epoch': 2.301710730948678,\n",
       "  'step': 2590},\n",
       " {'loss': 4.2284,\n",
       "  'grad_norm': 2.0876286029815674,\n",
       "  'learning_rate': 1.2759301942706619e-05,\n",
       "  'epoch': 2.310597644967785,\n",
       "  'step': 2600},\n",
       " {'loss': 4.2022,\n",
       "  'grad_norm': 2.7684569358825684,\n",
       "  'learning_rate': 1.259466578860718e-05,\n",
       "  'epoch': 2.319484558986892,\n",
       "  'step': 2610},\n",
       " {'loss': 4.2136,\n",
       "  'grad_norm': 2.1218128204345703,\n",
       "  'learning_rate': 1.2430029634507738e-05,\n",
       "  'epoch': 2.3283714730059986,\n",
       "  'step': 2620},\n",
       " {'loss': 4.2751,\n",
       "  'grad_norm': 2.4325311183929443,\n",
       "  'learning_rate': 1.2265393480408297e-05,\n",
       "  'epoch': 2.3372583870251056,\n",
       "  'step': 2630},\n",
       " {'loss': 4.3004,\n",
       "  'grad_norm': 2.031203269958496,\n",
       "  'learning_rate': 1.2100757326308859e-05,\n",
       "  'epoch': 2.3461453010442126,\n",
       "  'step': 2640},\n",
       " {'loss': 4.2061,\n",
       "  'grad_norm': 2.1571004390716553,\n",
       "  'learning_rate': 1.1936121172209418e-05,\n",
       "  'epoch': 2.355032215063319,\n",
       "  'step': 2650},\n",
       " {'loss': 4.2336,\n",
       "  'grad_norm': 2.597979784011841,\n",
       "  'learning_rate': 1.1771485018109977e-05,\n",
       "  'epoch': 2.363919129082426,\n",
       "  'step': 2660},\n",
       " {'loss': 4.1502,\n",
       "  'grad_norm': 2.0861475467681885,\n",
       "  'learning_rate': 1.1606848864010537e-05,\n",
       "  'epoch': 2.3728060431015328,\n",
       "  'step': 2670},\n",
       " {'loss': 4.2704,\n",
       "  'grad_norm': 3.247725009918213,\n",
       "  'learning_rate': 1.1442212709911098e-05,\n",
       "  'epoch': 2.3816929571206398,\n",
       "  'step': 2680},\n",
       " {'loss': 4.2578,\n",
       "  'grad_norm': 2.1407084465026855,\n",
       "  'learning_rate': 1.1277576555811657e-05,\n",
       "  'epoch': 2.390579871139747,\n",
       "  'step': 2690},\n",
       " {'loss': 4.2021,\n",
       "  'grad_norm': 2.144508123397827,\n",
       "  'learning_rate': 1.1112940401712217e-05,\n",
       "  'epoch': 2.399466785158854,\n",
       "  'step': 2700},\n",
       " {'loss': 4.2265,\n",
       "  'grad_norm': 2.351987600326538,\n",
       "  'learning_rate': 1.0948304247612776e-05,\n",
       "  'epoch': 2.4083536991779604,\n",
       "  'step': 2710},\n",
       " {'loss': 4.1552,\n",
       "  'grad_norm': 2.145186424255371,\n",
       "  'learning_rate': 1.0783668093513336e-05,\n",
       "  'epoch': 2.4172406131970674,\n",
       "  'step': 2720},\n",
       " {'loss': 4.203,\n",
       "  'grad_norm': 2.1409478187561035,\n",
       "  'learning_rate': 1.0619031939413895e-05,\n",
       "  'epoch': 2.426127527216174,\n",
       "  'step': 2730},\n",
       " {'loss': 4.2069,\n",
       "  'grad_norm': 2.0594663619995117,\n",
       "  'learning_rate': 1.0454395785314455e-05,\n",
       "  'epoch': 2.435014441235281,\n",
       "  'step': 2740},\n",
       " {'loss': 4.1778,\n",
       "  'grad_norm': 2.2129526138305664,\n",
       "  'learning_rate': 1.0289759631215016e-05,\n",
       "  'epoch': 2.443901355254388,\n",
       "  'step': 2750},\n",
       " {'loss': 4.2176,\n",
       "  'grad_norm': 2.8110995292663574,\n",
       "  'learning_rate': 1.0125123477115575e-05,\n",
       "  'epoch': 2.452788269273495,\n",
       "  'step': 2760},\n",
       " {'loss': 4.225,\n",
       "  'grad_norm': 2.1255743503570557,\n",
       "  'learning_rate': 9.960487323016135e-06,\n",
       "  'epoch': 2.4616751832926016,\n",
       "  'step': 2770},\n",
       " {'loss': 4.1739,\n",
       "  'grad_norm': 2.0709266662597656,\n",
       "  'learning_rate': 9.795851168916694e-06,\n",
       "  'epoch': 2.4705620973117086,\n",
       "  'step': 2780},\n",
       " {'loss': 4.2198,\n",
       "  'grad_norm': 2.3680856227874756,\n",
       "  'learning_rate': 9.631215014817255e-06,\n",
       "  'epoch': 2.479449011330815,\n",
       "  'step': 2790},\n",
       " {'loss': 4.2433,\n",
       "  'grad_norm': 2.3391597270965576,\n",
       "  'learning_rate': 9.466578860717815e-06,\n",
       "  'epoch': 2.488335925349922,\n",
       "  'step': 2800},\n",
       " {'loss': 4.1767,\n",
       "  'grad_norm': 2.9973559379577637,\n",
       "  'learning_rate': 9.301942706618374e-06,\n",
       "  'epoch': 2.4972228393690292,\n",
       "  'step': 2810},\n",
       " {'loss': 4.1833,\n",
       "  'grad_norm': 2.691636085510254,\n",
       "  'learning_rate': 9.137306552518934e-06,\n",
       "  'epoch': 2.5061097533881362,\n",
       "  'step': 2820},\n",
       " {'loss': 4.1138,\n",
       "  'grad_norm': 2.0133891105651855,\n",
       "  'learning_rate': 8.972670398419493e-06,\n",
       "  'epoch': 2.514996667407243,\n",
       "  'step': 2830},\n",
       " {'loss': 4.2714,\n",
       "  'grad_norm': 1.9475247859954834,\n",
       "  'learning_rate': 8.808034244320053e-06,\n",
       "  'epoch': 2.52388358142635,\n",
       "  'step': 2840},\n",
       " {'loss': 4.1424,\n",
       "  'grad_norm': 2.1982028484344482,\n",
       "  'learning_rate': 8.643398090220612e-06,\n",
       "  'epoch': 2.5327704954454564,\n",
       "  'step': 2850},\n",
       " {'loss': 4.193,\n",
       "  'grad_norm': 1.9643152952194214,\n",
       "  'learning_rate': 8.478761936121173e-06,\n",
       "  'epoch': 2.5416574094645634,\n",
       "  'step': 2860},\n",
       " {'loss': 4.1615,\n",
       "  'grad_norm': 2.003878355026245,\n",
       "  'learning_rate': 8.314125782021733e-06,\n",
       "  'epoch': 2.5505443234836704,\n",
       "  'step': 2870},\n",
       " {'loss': 4.1564,\n",
       "  'grad_norm': 3.05075740814209,\n",
       "  'learning_rate': 8.149489627922292e-06,\n",
       "  'epoch': 2.559431237502777,\n",
       "  'step': 2880},\n",
       " {'loss': 4.1475,\n",
       "  'grad_norm': 2.157576322555542,\n",
       "  'learning_rate': 7.984853473822852e-06,\n",
       "  'epoch': 2.568318151521884,\n",
       "  'step': 2890},\n",
       " {'loss': 4.1935,\n",
       "  'grad_norm': 2.1238512992858887,\n",
       "  'learning_rate': 7.820217319723413e-06,\n",
       "  'epoch': 2.577205065540991,\n",
       "  'step': 2900},\n",
       " {'loss': 4.126,\n",
       "  'grad_norm': 2.4104154109954834,\n",
       "  'learning_rate': 7.655581165623972e-06,\n",
       "  'epoch': 2.5860919795600976,\n",
       "  'step': 2910},\n",
       " {'loss': 4.2632,\n",
       "  'grad_norm': 2.530040979385376,\n",
       "  'learning_rate': 7.490945011524531e-06,\n",
       "  'epoch': 2.5949788935792046,\n",
       "  'step': 2920},\n",
       " {'loss': 4.2442,\n",
       "  'grad_norm': 2.406911611557007,\n",
       "  'learning_rate': 7.32630885742509e-06,\n",
       "  'epoch': 2.6038658075983117,\n",
       "  'step': 2930},\n",
       " {'loss': 4.2093,\n",
       "  'grad_norm': 2.688847780227661,\n",
       "  'learning_rate': 7.1616727033256504e-06,\n",
       "  'epoch': 2.6127527216174182,\n",
       "  'step': 2940},\n",
       " {'loss': 4.2381,\n",
       "  'grad_norm': 2.0510928630828857,\n",
       "  'learning_rate': 6.99703654922621e-06,\n",
       "  'epoch': 2.6216396356365252,\n",
       "  'step': 2950},\n",
       " {'loss': 4.18,\n",
       "  'grad_norm': 2.228199005126953,\n",
       "  'learning_rate': 6.83240039512677e-06,\n",
       "  'epoch': 2.630526549655632,\n",
       "  'step': 2960},\n",
       " {'loss': 4.204,\n",
       "  'grad_norm': 2.723302125930786,\n",
       "  'learning_rate': 6.66776424102733e-06,\n",
       "  'epoch': 2.639413463674739,\n",
       "  'step': 2970},\n",
       " {'loss': 4.1708,\n",
       "  'grad_norm': 2.4656529426574707,\n",
       "  'learning_rate': 6.50312808692789e-06,\n",
       "  'epoch': 2.648300377693846,\n",
       "  'step': 2980},\n",
       " {'loss': 4.0611,\n",
       "  'grad_norm': 2.0465149879455566,\n",
       "  'learning_rate': 6.338491932828449e-06,\n",
       "  'epoch': 2.657187291712953,\n",
       "  'step': 2990},\n",
       " {'loss': 4.219,\n",
       "  'grad_norm': 2.151592254638672,\n",
       "  'learning_rate': 6.173855778729009e-06,\n",
       "  'epoch': 2.6660742057320594,\n",
       "  'step': 3000},\n",
       " {'loss': 4.1574,\n",
       "  'grad_norm': 2.6070990562438965,\n",
       "  'learning_rate': 6.009219624629569e-06,\n",
       "  'epoch': 2.6749611197511665,\n",
       "  'step': 3010},\n",
       " {'loss': 4.2584,\n",
       "  'grad_norm': 2.1416687965393066,\n",
       "  'learning_rate': 5.8445834705301286e-06,\n",
       "  'epoch': 2.683848033770273,\n",
       "  'step': 3020},\n",
       " {'loss': 4.2212,\n",
       "  'grad_norm': 2.493741273880005,\n",
       "  'learning_rate': 5.679947316430689e-06,\n",
       "  'epoch': 2.69273494778938,\n",
       "  'step': 3030},\n",
       " {'loss': 4.1324,\n",
       "  'grad_norm': 2.178687572479248,\n",
       "  'learning_rate': 5.515311162331248e-06,\n",
       "  'epoch': 2.701621861808487,\n",
       "  'step': 3040},\n",
       " {'loss': 4.2382,\n",
       "  'grad_norm': 2.607297897338867,\n",
       "  'learning_rate': 5.350675008231808e-06,\n",
       "  'epoch': 2.710508775827594,\n",
       "  'step': 3050},\n",
       " {'loss': 3.9919,\n",
       "  'grad_norm': 2.4945454597473145,\n",
       "  'learning_rate': 5.186038854132367e-06,\n",
       "  'epoch': 2.7193956898467007,\n",
       "  'step': 3060},\n",
       " {'loss': 4.1217,\n",
       "  'grad_norm': 2.2347328662872314,\n",
       "  'learning_rate': 5.0214027000329275e-06,\n",
       "  'epoch': 2.7282826038658077,\n",
       "  'step': 3070},\n",
       " {'loss': 4.064,\n",
       "  'grad_norm': 2.326517105102539,\n",
       "  'learning_rate': 4.856766545933488e-06,\n",
       "  'epoch': 2.7371695178849142,\n",
       "  'step': 3080},\n",
       " {'loss': 4.131,\n",
       "  'grad_norm': 3.2224950790405273,\n",
       "  'learning_rate': 4.692130391834047e-06,\n",
       "  'epoch': 2.7460564319040213,\n",
       "  'step': 3090},\n",
       " {'loss': 4.206,\n",
       "  'grad_norm': 3.053706169128418,\n",
       "  'learning_rate': 4.527494237734607e-06,\n",
       "  'epoch': 2.7549433459231283,\n",
       "  'step': 3100},\n",
       " {'loss': 4.2881,\n",
       "  'grad_norm': 2.3909480571746826,\n",
       "  'learning_rate': 4.362858083635166e-06,\n",
       "  'epoch': 2.7638302599422353,\n",
       "  'step': 3110},\n",
       " {'loss': 4.1475,\n",
       "  'grad_norm': 2.4445784091949463,\n",
       "  'learning_rate': 4.1982219295357264e-06,\n",
       "  'epoch': 2.772717173961342,\n",
       "  'step': 3120},\n",
       " {'loss': 4.1779,\n",
       "  'grad_norm': 2.304943323135376,\n",
       "  'learning_rate': 4.033585775436286e-06,\n",
       "  'epoch': 2.781604087980449,\n",
       "  'step': 3130},\n",
       " {'loss': 4.1397,\n",
       "  'grad_norm': 2.3117282390594482,\n",
       "  'learning_rate': 3.868949621336846e-06,\n",
       "  'epoch': 2.7904910019995555,\n",
       "  'step': 3140},\n",
       " {'loss': 4.2405,\n",
       "  'grad_norm': 2.443709373474121,\n",
       "  'learning_rate': 3.7043134672374052e-06,\n",
       "  'epoch': 2.7993779160186625,\n",
       "  'step': 3150},\n",
       " {'loss': 4.1358,\n",
       "  'grad_norm': 2.0286383628845215,\n",
       "  'learning_rate': 3.539677313137965e-06,\n",
       "  'epoch': 2.8082648300377695,\n",
       "  'step': 3160},\n",
       " {'loss': 4.1589,\n",
       "  'grad_norm': 2.2737700939178467,\n",
       "  'learning_rate': 3.375041159038525e-06,\n",
       "  'epoch': 2.8171517440568765,\n",
       "  'step': 3170},\n",
       " {'loss': 4.1953,\n",
       "  'grad_norm': 2.6672463417053223,\n",
       "  'learning_rate': 3.210405004939085e-06,\n",
       "  'epoch': 2.826038658075983,\n",
       "  'step': 3180},\n",
       " {'loss': 4.2151,\n",
       "  'grad_norm': 2.1402323246002197,\n",
       "  'learning_rate': 3.0457688508396443e-06,\n",
       "  'epoch': 2.83492557209509,\n",
       "  'step': 3190},\n",
       " {'loss': 4.3077,\n",
       "  'grad_norm': 2.481356143951416,\n",
       "  'learning_rate': 2.881132696740204e-06,\n",
       "  'epoch': 2.8438124861141967,\n",
       "  'step': 3200},\n",
       " {'loss': 4.1325,\n",
       "  'grad_norm': 2.170210123062134,\n",
       "  'learning_rate': 2.716496542640764e-06,\n",
       "  'epoch': 2.8526994001333037,\n",
       "  'step': 3210},\n",
       " {'loss': 4.1478,\n",
       "  'grad_norm': 2.3700592517852783,\n",
       "  'learning_rate': 2.551860388541324e-06,\n",
       "  'epoch': 2.8615863141524107,\n",
       "  'step': 3220},\n",
       " {'loss': 4.1955,\n",
       "  'grad_norm': 2.932849645614624,\n",
       "  'learning_rate': 2.3872242344418838e-06,\n",
       "  'epoch': 2.8704732281715173,\n",
       "  'step': 3230},\n",
       " {'loss': 4.1396,\n",
       "  'grad_norm': 2.0267951488494873,\n",
       "  'learning_rate': 2.2225880803424432e-06,\n",
       "  'epoch': 2.8793601421906243,\n",
       "  'step': 3240},\n",
       " {'loss': 4.1476,\n",
       "  'grad_norm': 2.3862361907958984,\n",
       "  'learning_rate': 2.057951926243003e-06,\n",
       "  'epoch': 2.8882470562097313,\n",
       "  'step': 3250},\n",
       " {'loss': 4.2036,\n",
       "  'grad_norm': 2.5772182941436768,\n",
       "  'learning_rate': 1.8933157721435627e-06,\n",
       "  'epoch': 2.897133970228838,\n",
       "  'step': 3260},\n",
       " {'loss': 4.1773,\n",
       "  'grad_norm': 3.2005748748779297,\n",
       "  'learning_rate': 1.7286796180441226e-06,\n",
       "  'epoch': 2.906020884247945,\n",
       "  'step': 3270},\n",
       " {'loss': 4.1281,\n",
       "  'grad_norm': 3.4535269737243652,\n",
       "  'learning_rate': 1.5640434639446825e-06,\n",
       "  'epoch': 2.914907798267052,\n",
       "  'step': 3280},\n",
       " {'loss': 4.1085,\n",
       "  'grad_norm': 2.5282809734344482,\n",
       "  'learning_rate': 1.3994073098452421e-06,\n",
       "  'epoch': 2.9237947122861585,\n",
       "  'step': 3290},\n",
       " {'loss': 4.187,\n",
       "  'grad_norm': 3.1048123836517334,\n",
       "  'learning_rate': 1.2347711557458018e-06,\n",
       "  'epoch': 2.9326816263052655,\n",
       "  'step': 3300},\n",
       " {'loss': 4.0735,\n",
       "  'grad_norm': 2.691267251968384,\n",
       "  'learning_rate': 1.0701350016463617e-06,\n",
       "  'epoch': 2.941568540324372,\n",
       "  'step': 3310},\n",
       " {'loss': 4.1548,\n",
       "  'grad_norm': 2.498093843460083,\n",
       "  'learning_rate': 9.054988475469213e-07,\n",
       "  'epoch': 2.950455454343479,\n",
       "  'step': 3320},\n",
       " {'loss': 4.1543,\n",
       "  'grad_norm': 2.129580020904541,\n",
       "  'learning_rate': 7.408626934474811e-07,\n",
       "  'epoch': 2.959342368362586,\n",
       "  'step': 3330},\n",
       " {'loss': 4.17,\n",
       "  'grad_norm': 2.302260637283325,\n",
       "  'learning_rate': 5.762265393480408e-07,\n",
       "  'epoch': 2.968229282381693,\n",
       "  'step': 3340},\n",
       " {'loss': 4.1334,\n",
       "  'grad_norm': 2.2644286155700684,\n",
       "  'learning_rate': 4.1159038524860064e-07,\n",
       "  'epoch': 2.9771161964007997,\n",
       "  'step': 3350},\n",
       " {'loss': 4.1504,\n",
       "  'grad_norm': 2.605426788330078,\n",
       "  'learning_rate': 2.469542311491604e-07,\n",
       "  'epoch': 2.9860031104199067,\n",
       "  'step': 3360},\n",
       " {'loss': 4.1784,\n",
       "  'grad_norm': 2.928342580795288,\n",
       "  'learning_rate': 8.231807704972012e-08,\n",
       "  'epoch': 2.9948900244390133,\n",
       "  'step': 3370},\n",
       " {'eval_loss': 4.227635860443115,\n",
       "  'eval_accuracy': 0.27546556680414946,\n",
       "  'eval_runtime': 76.5679,\n",
       "  'eval_samples_per_second': 104.495,\n",
       "  'eval_steps_per_second': 13.073,\n",
       "  'epoch': 2.9993334814485673,\n",
       "  'step': 3375},\n",
       " {'train_runtime': 6798.0282,\n",
       "  'train_samples_per_second': 31.775,\n",
       "  'train_steps_per_second': 0.496,\n",
       "  'total_flos': 1.678650555446478e+19,\n",
       "  'train_loss': 4.755938376532661,\n",
       "  'epoch': 2.9993334814485673,\n",
       "  'step': 3375},\n",
       " {'eval_loss': 4.233978748321533,\n",
       "  'eval_accuracy': 0.27034120734908135,\n",
       "  'eval_runtime': 95.2531,\n",
       "  'eval_samples_per_second': 83.997,\n",
       "  'eval_steps_per_second': 10.509,\n",
       "  'epoch': 2.9993334814485673,\n",
       "  'step': 3375}]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.state.log_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object <genexpr> at 0x7fe3ef2c5040>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[print(x[\"loss\"] for x in trainer.state.log_history)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the increase in accracy over time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "losses = [x[\"loss\"] for x in trainer.state.log_history if \"loss\" in x]\n",
    "\n",
    "plt.plot(losses)\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = weeds[\"validation\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'label': 0,\n",
       " 'pixel_values': tensor([[[-0.4980, -0.5216, -0.5686,  ..., -0.1451, -0.1686, -0.2078],\n",
       "          [-0.4745, -0.4980, -0.5451,  ..., -0.1686, -0.2078, -0.2314],\n",
       "          [-0.4510, -0.4745, -0.5294,  ..., -0.2000, -0.2392, -0.2627],\n",
       "          ...,\n",
       "          [-0.3176, -0.3098, -0.2941,  ..., -0.4980, -0.5137, -0.5059],\n",
       "          [-0.3098, -0.3020, -0.2863,  ..., -0.5216, -0.5373, -0.5373],\n",
       "          [-0.3176, -0.3098, -0.3020,  ..., -0.5451, -0.5765, -0.5843]],\n",
       " \n",
       "         [[-0.2627, -0.2863, -0.3176,  ...,  0.1686,  0.1451,  0.1059],\n",
       "          [-0.2392, -0.2627, -0.3020,  ...,  0.1451,  0.1059,  0.0824],\n",
       "          [-0.2157, -0.2392, -0.2863,  ...,  0.1137,  0.0745,  0.0510],\n",
       "          ...,\n",
       "          [-0.1922, -0.1843, -0.1686,  ..., -0.2863, -0.2941, -0.2941],\n",
       "          [-0.1922, -0.1843, -0.1686,  ..., -0.3098, -0.3255, -0.3412],\n",
       "          [-0.2000, -0.1922, -0.1843,  ..., -0.3333, -0.3647, -0.3882]],\n",
       " \n",
       "         [[-0.7333, -0.7569, -0.8039,  ..., -0.5294, -0.5529, -0.5922],\n",
       "          [-0.7255, -0.7490, -0.7961,  ..., -0.5529, -0.5922, -0.6157],\n",
       "          [-0.7020, -0.7255, -0.7725,  ..., -0.5843, -0.6235, -0.6471],\n",
       "          ...,\n",
       "          [-0.6235, -0.6157, -0.6157,  ..., -0.7412, -0.7412, -0.7412],\n",
       "          [-0.6235, -0.6157, -0.6157,  ..., -0.7490, -0.7569, -0.7725],\n",
       "          [-0.6314, -0.6235, -0.6314,  ..., -0.7647, -0.7961, -0.8196]]])}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = load_dataset(\"n1hal/Capstone_Weeds\", split=\"validation[:10]\")\n",
    "image = ds[\"image\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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",
      "image/png": "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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x256>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "classifier = pipeline(\"image-classification\", model = \"Weeds_Classification\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'Brachiaria eruciformis J.E.Smith Griseb.',\n",
       "  'score': 0.7283827066421509},\n",
       " {'label': 'Asteracantha longifolia L. Nees.', 'score': 0.7247171401977539},\n",
       " {'label': 'Volutarella divaricata Benth  Hook.', 'score': 0.7182118892669678},\n",
       " {'label': 'Convolvulus pluricaulis Choisy.', 'score': 0.7177550792694092},\n",
       " {'label': 'Sebastiana chamaelea Linn. Muell.', 'score': 0.7168266773223877}]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier(image)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf-gpu",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}