French_Bambara.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lGGRAn0vJk1X"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QphjaI1KJl3Q",
    "outputId": "fb326bd3-4125-48fe-a387-7ab1091276fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in /Users/michael/miniconda3/lib/python3.9/site-packages (4.26.1)\n",
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      "Requirement already satisfied: requests in /Users/michael/miniconda3/lib/python3.9/site-packages (from transformers) (2.27.1)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /Users/michael/miniconda3/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.4.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/michael/miniconda3/lib/python3.9/site-packages (from packaging>=20.0->transformers) (3.0.9)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/michael/miniconda3/lib/python3.9/site-packages (from requests->transformers) (1.26.9)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/michael/miniconda3/lib/python3.9/site-packages (from requests->transformers) (3.3)\n",
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     ]
    }
   ],
   "source": [
    "! pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ozm-09e3JEye",
    "outputId": "81444fa3-2819-4b47-9614-c7dfaffc51d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: huggingface_hub in /Users/michael/miniconda3/lib/python3.9/site-packages (0.12.0)\n",
      "Requirement already satisfied: requests in /Users/michael/miniconda3/lib/python3.9/site-packages (from huggingface_hub) (2.27.1)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /Users/michael/miniconda3/lib/python3.9/site-packages (from requests->huggingface_hub) (2022.9.24)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/michael/miniconda3/lib/python3.9/site-packages (from requests->huggingface_hub) (3.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/michael/miniconda3/lib/python3.9/site-packages (from requests->huggingface_hub) (1.26.9)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /Users/michael/miniconda3/lib/python3.9/site-packages (from requests->huggingface_hub) (2.0.4)\n"
     ]
    }
   ],
   "source": [
    "! pip install --upgrade huggingface_hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331,
     "referenced_widgets": [
      "38a22dc6fdaa440987d18e0e34dabd19",
      "cf729ca682f24e65badf42cb41a9c690",
      "36a6d3989188477d95cd505d3d3d424d",
      "52fea45b35d3477982a9883e1c385b38",
      "28a95d6a1fc648d081d1477f43b4abc6",
      "5e2f118c4e2f423fba0b26c9da271ed0",
      "4110725dc18249189a53d8da0486ed3c",
      "d452701cb2b348778b091f36ed1b7cea",
      "7387bb85e2d74f79a8619e0be244b432",
      "ba196bb8af154cf79fe3fe70f0ca08b3",
      "f3c14bda14e84651a09af1228d165f2f",
      "387f8200ce634b39b0abe0546f901578",
      "a649cac4a0c54b1f83b6c04c36b25b96",
      "54d043d9a4b940369fff9858a742e3f7",
      "9ce5f9664cc24857ac385a1446f2dad2",
      "a178dc772cb84327af00fcc74ade7dff",
      "0bcdd598766e471dbb6bd8094158cb63"
     ]
    },
    "id": "h7F9EgYPIUC1",
    "outputId": "bca006e5-4634-49e0-cf6f-fc9d654141ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: six>=1.5 in /Users/michael/miniconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.12->ipykernel>=4.5.1->ipywidgets) (1.16.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/michael/miniconda3/lib/python3.9/site-packages (from packaging->ipykernel>=4.5.1->ipywidgets) (3.0.9)\n",
      "Requirement already satisfied: asttokens>=2.1.0 in /Users/michael/miniconda3/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.1.0)\n",
      "Requirement already satisfied: pure-eval in /Users/michael/miniconda3/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n",
      "Requirement already satisfied: executing>=1.2.0 in /Users/michael/miniconda3/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (1.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install ipywidgets\n",
    "from ipywidgets import widgets, interactive\n",
    "from huggingface_hub import notebook_login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GssVs-lsGKqC",
    "outputId": "66bbb2ec-298c-49d3-f01a-7abe21d8d1ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in /Users/michael/miniconda3/lib/python3.9/site-packages (2.9.0)\n",
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      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /Users/michael/miniconda3/lib/python3.9/site-packages (from aiohttp->datasets) (4.0.2)\n",
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      "Requirement already satisfied: attrs>=17.3.0 in /Users/michael/miniconda3/lib/python3.9/site-packages (from aiohttp->datasets) (22.1.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /Users/michael/miniconda3/lib/python3.9/site-packages (from aiohttp->datasets) (1.3.3)\n",
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      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /Users/michael/miniconda3/lib/python3.9/site-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (4.4.0)\n",
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      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/michael/miniconda3/lib/python3.9/site-packages (from requests>=2.19.0->datasets) (1.26.9)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/michael/miniconda3/lib/python3.9/site-packages (from requests>=2.19.0->datasets) (3.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/michael/miniconda3/lib/python3.9/site-packages (from pandas->datasets) (2022.6)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/michael/miniconda3/lib/python3.9/site-packages (from pandas->datasets) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/michael/miniconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WOAOCjKLGK6Z"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N65MVRgSVEen",
    "outputId": "77341144-deac-4e1d-9181-47198c040619"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.26.1\n"
     ]
    }
   ],
   "source": [
    "import transformers\n",
    "\n",
    "print(transformers.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "m4dD01Wgic_-"
   },
   "outputs": [],
   "source": [
    "from transformers.utils import send_example_telemetry\n",
    "\n",
    "send_example_telemetry(\"translation_notebook\", framework=\"tf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Un_xKhU-2rSD"
   },
   "outputs": [],
   "source": [
    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-ROMANCE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qGfgnjj8xMEK",
    "outputId": "ebf58c07-0b9c-4f93-8bb3-e7c7e61aeb3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: no matches found: transformers[sentencepiece]\n",
      "Requirement already satisfied: sacrebleu in /Users/michael/miniconda3/lib/python3.9/site-packages (2.3.1)\n",
      "Requirement already satisfied: colorama in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacrebleu) (0.4.6)\n",
      "Requirement already satisfied: regex in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacrebleu) (2022.10.31)\n",
      "Requirement already satisfied: tabulate>=0.8.9 in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacrebleu) (0.9.0)\n",
      "Requirement already satisfied: portalocker in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacrebleu) (2.7.0)\n",
      "Requirement already satisfied: numpy>=1.17 in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacrebleu) (1.23.4)\n",
      "Requirement already satisfied: lxml in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacrebleu) (4.9.2)\n"
     ]
    }
   ],
   "source": [
    "! pip install datasets transformers[sentencepiece] sacrebleu\n",
    "! pip install sacrebleu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Cn5m9bL6z5n0"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset, load_metric\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 140,
     "referenced_widgets": [
      "ad8297b18c4141b799e1a9c02a4d6682",
      "b616968f83fe4d28a5a46a240c974e15",
      "6b28c992f2074c089597d6a4594dedcb",
      "b45033c464dd40948a0ecd7c88db2917",
      "126f457df18f49bf9151c1c8825e3b5b",
      "7b9b1a80652f4797acdb0f867b971d7e",
      "3530983981e6470bafd4d39d1d6e1795",
      "1c708e6353ca4ff1bdbd868de838b969",
      "58f4af96c9e14925949e2f2c8faca933",
      "44e061e7aae34b8db848d8a43c130590",
      "bf173ae670c442e6b7519fd7d33cd77e"
     ]
    },
    "id": "qJ4mf2fDjfIv",
    "outputId": "b8b51c4e-5d96-487f-8200-1b1c23d732ca"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vq/qfqzj01x6ms_n2n90qm33bvr0000gn/T/ipykernel_33828/567929508.py:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
      "  metric = load_metric(\"sacrebleu\")\n",
      "Using custom data configuration default-1d10f823a78c2103\n",
      "Found cached dataset json (/Users/michael/.cache/huggingface/datasets/json/default-1d10f823a78c2103/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n"
     ]
    },
    {
     "data": {
      "application/json": {
       "ascii": false,
       "bar_format": null,
       "colour": null,
       "elapsed": 0.011876106262207031,
       "initial": 0,
       "n": 0,
       "ncols": null,
       "nrows": 24,
       "postfix": null,
       "prefix": "",
       "rate": null,
       "total": 3,
       "unit": "it",
       "unit_divisor": 1000,
       "unit_scale": false
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c4857f8f59844959f9ecd5e582c1e50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "metric = load_metric(\"sacrebleu\")\n",
    "\n",
    "raw_dataset = load_dataset('json', data_files={'train': ['content/output.jsonl', 'content/output2flores.jsonl', 'content/output3org.jsonl', 'content/output4med.jsonl', 'content/output5mafand.jsonl', 'content/output6bay.jsonl'], 'validation': 'content/dev.jsonl', 'test': 'content/test.jsonl'})\n",
    "#excluded '/content/train.jsonl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cnx7h42TjmUu",
    "outputId": "f32bf3f5-387c-4903-cfb7-a8652040825f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['translation'],\n",
       "        num_rows: 64341\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['translation'],\n",
       "        num_rows: 268\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['translation'],\n",
       "        num_rows: 228\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2tWSDnBrjq95",
    "outputId": "9fcdee3b-2a3f-495b-a82d-39ca766e6866"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'translation': {'bam': 'Lilian Diana (Ɔkutɔburukalo tile tan 14, san 1893 – fewuruye tile 27, san 1993) ye Ameriki filimu kela, kuntigi ani siniman glana de kun do.',\n",
       "  'fr': 'Lillian Diana Gish (14 octobre 1893 - 27 février 1993) était une actrice, réalisatrice et scénariste américaine.'}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_dataset[\"train\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "F6fv_v38j_B8"
   },
   "outputs": [],
   "source": [
    "import datasets\n",
    "import random\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "def show_random_elements(dataset, num_examples=5):\n",
    "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
    "    picks = []\n",
    "    for _ in range(num_examples):\n",
    "        pick = random.randint(0, len(dataset)-1)\n",
    "        while pick in picks:\n",
    "            pick = random.randint(0, len(dataset)-1)\n",
    "        picks.append(pick)\n",
    "    \n",
    "    df = pd.DataFrame(dataset[picks])\n",
    "    for column, typ in dataset.features.items():\n",
    "        if isinstance(typ, datasets.ClassLabel):\n",
    "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
    "    display(HTML(df.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "7F-IwAp1kCo_",
    "outputId": "f8eeaa4c-3940-4945-bcc6-fddd8901600d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>translation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'bam': '6', 'fr': 'Chapitre 6'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'bam': 'Muso ka malo siw sɛnɛta.', 'fr': 'Les variétés locales des femmes, bien meilleures'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'bam': 'U matigi fana bɛ misi di u ma.', 'fr': 'et en échange, leur maître leur donne une vache.'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'bam': 'A ko mun don?', 'fr': 'lle dit : « Qu'est-ce que c'est ?'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'bam': 'Min bɛ u ni san cɛ, mɔgɔw kɔni bɛ u kali o de Tɔgɔ la, ani u ni ɲɔgɔn cɛ la ; o kalili ye ko jɛlen de ye min bɛ sɔsɔli bɛɛ ban.', 'fr': 'Les hommes jurent par un plus grand, et, entre eux, la garantie du serment met un terme à toute contestation.'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_random_elements(raw_dataset[\"train\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SP3rDCB1koK0",
    "outputId": "15f331f4-0921-4b5a-afcc-e5f6ba6690e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Metric(name: \"sacrebleu\", features: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')}, usage: \"\"\"\n",
       "Produces BLEU scores along with its sufficient statistics\n",
       "from a source against one or more references.\n",
       "\n",
       "Args:\n",
       "    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n",
       "    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n",
       "    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n",
       "        - `'none'`: no smoothing\n",
       "        - `'floor'`: increment zero counts\n",
       "        - `'add-k'`: increment num/denom by k for n>1\n",
       "        - `'exp'`: exponential decay\n",
       "    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n",
       "    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n",
       "        - `'none'`: No tokenization.\n",
       "        - `'zh'`: Chinese tokenization.\n",
       "        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n",
       "        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n",
       "        - `'char'`: Language-agnostic character-level tokenization.\n",
       "        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n",
       "    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n",
       "    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n",
       "    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n",
       "\n",
       "Returns:\n",
       "    'score': BLEU score,\n",
       "    'counts': Counts,\n",
       "    'totals': Totals,\n",
       "    'precisions': Precisions,\n",
       "    'bp': Brevity penalty,\n",
       "    'sys_len': predictions length,\n",
       "    'ref_len': reference length,\n",
       "\n",
       "Examples:\n",
       "\n",
       "    Example 1:\n",
       "        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n",
       "        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n",
       "        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n",
       "        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n",
       "        >>> print(list(results.keys()))\n",
       "        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n",
       "        >>> print(round(results[\"score\"], 1))\n",
       "        100.0\n",
       "\n",
       "    Example 2:\n",
       "        >>> predictions = [\"hello there general kenobi\",\n",
       "        ...                 \"on our way to ankh morpork\"]\n",
       "        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n",
       "        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n",
       "        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n",
       "        >>> results = sacrebleu.compute(predictions=predictions,\n",
       "        ...                             references=references)\n",
       "        >>> print(list(results.keys()))\n",
       "        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n",
       "        >>> print(round(results[\"score\"], 1))\n",
       "        39.8\n",
       "\"\"\", stored examples: 0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nY3WLKPCkq8F",
    "outputId": "1ef46f62-952e-467c-f811-7a9049c25c22"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'score': 0.0,\n",
       " 'counts': [4, 2, 0, 0],\n",
       " 'totals': [4, 2, 0, 0],\n",
       " 'precisions': [100.0, 100.0, 0.0, 0.0],\n",
       " 'bp': 1.0,\n",
       " 'sys_len': 4,\n",
       " 'ref_len': 4}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fake_preds = [\"hello there\", \"general kenobi\"]\n",
    "fake_labels = [[\"hello there\"], [\"general kenobi\"]]\n",
    "metric.compute(predictions=fake_preds, references=fake_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yJjvgTzblChu",
    "outputId": "92196613-5b61-47db-bfab-a76e7e1ed3ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sentencepiece in /Users/michael/miniconda3/lib/python3.9/site-packages (0.1.97)\n",
      "Requirement already satisfied: sacremoses in /Users/michael/miniconda3/lib/python3.9/site-packages (0.0.53)\n",
      "Requirement already satisfied: click in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacremoses) (8.1.3)\n",
      "Requirement already satisfied: regex in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacremoses) (2022.10.31)\n",
      "Requirement already satisfied: tqdm in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacremoses) (4.64.0)\n",
      "Requirement already satisfied: six in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacremoses) (1.16.0)\n",
      "Requirement already satisfied: joblib in /Users/michael/miniconda3/lib/python3.9/site-packages (from sacremoses) (1.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install sentencepiece\n",
    "!pip install sacremoses\n",
    "from transformers import AutoTokenizer\n",
    "    \n",
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "lEbS9HW93AJ1"
   },
   "outputs": [],
   "source": [
    "if \"mbart\" in model_checkpoint:\n",
    "    tokenizer.src_lang = \"bam-XX\"\n",
    "    tokenizer.tgt_lang = \"fr-FR\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "TR3pgK7o3FYo"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [4708, 2, 69, 28, 9, 8662, 84, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer(\"Hello, this is a sentence!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "AEWp4H493I24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [[4708, 2, 69, 28, 9, 8662, 84, 0], [188, 28, 823, 8662, 3, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer([\"Hello, this is a sentence!\", \"This is another sentence.\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Xk7DMhnn3LR6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input_ids': [[14232, 244, 2, 69, 160, 6, 9, 10513, 1101, 84, 0], [13486, 6, 160, 6, 3778, 4853, 10513, 1101, 3, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/michael/miniconda3/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:3581: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "with tokenizer.as_target_tokenizer():\n",
    "    print(tokenizer([\"Hello, this is a sentence!\", \"This is another sentence.\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "cnxUr--H3OXs"
   },
   "outputs": [],
   "source": [
    "if model_checkpoint in [\"t5-small\", \"t5-base\", \"t5-larg\", \"t5-3b\", \"t5-11b\"]:\n",
    "    prefix = \"translate English to Romanian: \"\n",
    "else:\n",
    "    prefix = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "yrZg7XCE3RTw"
   },
   "outputs": [],
   "source": [
    "max_input_length = 128\n",
    "max_target_length = 128\n",
    "source_lang = \"bam\"\n",
    "target_lang = \"fr\"\n",
    "\n",
    "\n",
    "def preprocess_function(examples):\n",
    "    inputs = [prefix + ex[source_lang] for ex in examples[\"translation\"]]\n",
    "    targets = [ex[target_lang] for ex in examples[\"translation\"]]\n",
    "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
    "\n",
    "    # Setup the tokenizer for targets\n",
    "    with tokenizer.as_target_tokenizer():\n",
    "        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
    "\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
    "    return model_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "FxdByiFe3U1o"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [[46157, 440, 42575, 23, 54309, 7027, 47, 52272, 6106, 41139, 244, 41081, 2240, 12513, 5525, 337, 3936, 375, 1129, 19275, 4919, 41081, 20102, 5525, 3271, 39, 8758, 67, 1633, 28361, 6815, 1479, 190, 23218, 242, 2, 2573, 184, 47, 21504, 90, 75, 108, 2630, 677, 21523, 326, 5, 2573, 184, 79, 3, 0], [424, 3531, 8758, 279, 1566, 47, 14835, 23218, 242, 7014, 2466, 1222, 79, 5, 5525, 49470, 90, 75, 5525, 36823, 853, 42, 53771, 76, 3818, 127, 2, 9, 8758, 9739, 12746, 3183, 815, 5189, 2573, 184, 47, 21504, 3926, 12, 605, 8758, 307, 3, 739, 3, 8397, 4419, 365, 13649, 750, 5659, 724, 11, 3, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'labels': [[150, 11717, 440, 5814, 326, 424, 3531, 13576, 3566, 337, 3936, 25, 933, 4011, 3271, 39, 879, 99, 9, 34622, 2, 37416, 6864, 20, 108, 3501, 5699, 2334, 16189, 3, 0], [424, 3531, 879, 99, 1865, 65, 5, 22272, 5, 1987, 509, 5, 471, 2559, 146, 1928, 471, 1489, 2, 3670, 42, 4676, 24145, 146, 9272, 52, 44579, 307, 739, 8397, 4419, 365, 3, 0]]}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess_function(raw_dataset[\"train\"][:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "K20Yjaot3pWf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /Users/michael/.cache/huggingface/datasets/json/default-1d10f823a78c2103/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-5df8d0e1aa39b6be.arrow\n",
      "Loading cached processed dataset at /Users/michael/.cache/huggingface/datasets/json/default-1d10f823a78c2103/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-43f434706d963a93.arrow\n",
      "Loading cached processed dataset at /Users/michael/.cache/huggingface/datasets/json/default-1d10f823a78c2103/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-1f435a57217cd7e8.arrow\n"
     ]
    }
   ],
   "source": [
    "tokenized_datasets = raw_dataset.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "jKVGI18034HV"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1 Pro\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-14 17:08:41.681583: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2023-02-14 17:08:41.681997: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
      "All model checkpoint layers were used when initializing TFMarianMTModel.\n",
      "\n",
      "All the layers of TFMarianMTModel were initialized from the model checkpoint at Helsinki-NLP/opus-mt-en-ROMANCE.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFMarianMTModel for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "from transformers import TFAutoModelForSeq2SeqLM, DataCollatorForSeq2Seq\n",
    "\n",
    "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "Xm98xT5M37ZD"
   },
   "outputs": [],
   "source": [
    "batch_size = 4096 #was 10 before\n",
    "learning_rate = 1 #2e-5\n",
    "weight_decay = 0.0001 #?\n",
    "num_train_epochs = 1 #1 before\n",
    "\n",
    "model_name = model_checkpoint.split(\"/\")[-1]\n",
    "push_to_hub_model_id = f\"{model_name}-finetuned-{source_lang}-to-{target_lang}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "eDu8EHOd3_OC"
   },
   "outputs": [],
   "source": [
    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"np\")\n",
    "\n",
    "generation_data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"np\", pad_to_multiple_of=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tORu7nAqieZu"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "4C12FtYp4CIF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in /Users/michael/miniconda3/lib/python3.9/site-packages (2.9.0)\n",
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     ]
    }
   ],
   "source": [
    "!pip install datasets\n",
    "train_dataset = model.prepare_tf_dataset(\n",
    "    tokenized_datasets[\"train\"],\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    "    collate_fn=data_collator,\n",
    ")\n",
    "\n",
    "validation_dataset = model.prepare_tf_dataset(\n",
    "    tokenized_datasets[\"validation\"],\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,\n",
    "    collate_fn=data_collator,\n",
    ")\n",
    "\n",
    "generation_dataset = model.prepare_tf_dataset(\n",
    "    tokenized_datasets[\"validation\"],\n",
    "    batch_size=8,\n",
    "    shuffle=False,\n",
    "    collate_fn=generation_data_collator,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "g-7vYn1U4DBf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AdamWeightDecay\n",
    "import tensorflow as tf\n",
    "\n",
    "optimizer = AdamWeightDecay(learning_rate=learning_rate, weight_decay_rate=weight_decay)\n",
    "model.compile(optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "-YFzWI144OHC"
   },
   "outputs": [],
   "source": [
    "from transformers.keras_callbacks import KerasMetricCallback\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def metric_fn(eval_predictions):\n",
    "    preds, labels = eval_predictions\n",
    "    prediction_lens = [\n",
    "        np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds\n",
    "    ]\n",
    "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
    "\n",
    "    # We use -100 to mask labels - replace it with the tokenizer pad token when decoding\n",
    "    # so that no output is emitted for these\n",
    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
    "\n",
    "    # Some simple post-processing\n",
    "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
    "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
    "\n",
    "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
    "    result = {\"bleu\": result[\"score\"]}\n",
    "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
    "    return result\n",
    "\n",
    "\n",
    "metric_callback = KerasMetricCallback(\n",
    "    metric_fn=metric_fn, eval_dataset=generation_dataset, predict_with_generate=True, use_xla_generation=True, \n",
    "    generate_kwargs={\"max_length\": 128}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YnRSNKyp4OkT"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-14 17:11:15.497634: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2023-02-14 17:11:27.052249: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
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   ],
   "source": [
    "from transformers.keras_callbacks import PushToHubCallback\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "\n",
    "tensorboard_callback = TensorBoard(log_dir=\"./translation_model_save/logs\")\n",
    "\n",
    "push_to_hub_callback = PushToHubCallback(\n",
    "    output_dir=\"./translation_model_save\",\n",
    "    tokenizer=tokenizer,\n",
    "    hub_model_id=push_to_hub_model_id,\n",
    ")\n",
    "\n",
    "callbacks = [metric_callback, tensorboard_callback, push_to_hub_callback]\n",
    "\n",
    "model.fit(\n",
    "    train_dataset, validation_data=validation_dataset, epochs=1000, callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aEA1sqzM4bOp"
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   "source": [
    "from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM\n",
    "\n",
    "# You can of course substitute your own username and model here if you've trained and uploaded it!\n",
    "model_name = 'mpham8/opus-mt-en-ROMANCE-finetuned-bam-to-fr'\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VMJn6kFg4dFE"
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   "source": [
    "input_text  = \"Farigan t'a la.\"\n",
    "#if 't5' in model_name: \n",
    "    #input_text = \"translate English to Romanian: \" + input_text\n",
    "tokenized = tokenizer([input_text], return_tensors='np')\n",
    "out = model.generate(**tokenized, max_length=128)\n",
    "print(out)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B6jZsSTx4fHf"
   },
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   "source": [
    "with tokenizer.as_target_tokenizer():\n",
    "    print(tokenizer.decode(out[0], skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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