notebook_gnner.ipynb

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   "source": [
    "from models import GnnerAT\n",
    "from trainer import LightningWrapper\n",
    "import pickle\n",
    "import pytorch_lightning as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=['Method', 'Material', 'Task', 'Generic', 'OtherScientificTerm', 'Metric']\n",
    "encoder='allenai/scibert_scivocab_uncased'\n",
    "path = 'scierc_dataset.pk'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path, 'rb') as f:\n",
    "    data = pickle.load(f)\n",
    "    dev_data = [i.values() for i in data['dev']]\n",
    "    train_data = [i.values() for i in data['train']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
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    "model = GnnerAT(labels=labels)"
   ]
  },
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   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = model.create_dataloader(train_data, batch_size=4, num_workers=4, shuffle=False)\n",
    "val_loader = model.create_dataloader(dev_data, batch_size=4, num_workers=4, shuffle=False)"
   ]
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   "execution_count": 10,
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    "lightning = LightningWrapper(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# logging\n",
    "use_wandb = True\n",
    "\n",
    "if use_wandb:\n",
    "    from pytorch_lightning.loggers import WandbLogger\n",
    "    logger = WandbLogger(project=\"GNNer\")\n",
    "else:\n",
    "    logger = None"
   ]
  },
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   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n"
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    "trainer = pl.Trainer(logger=logger, max_epochs=40, gpus=1)"
   ]
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      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name  | Type    | Params\n",
      "----------------------------------\n",
      "0 | model | GnnerAT | 111 M \n",
      "----------------------------------\n",
      "111 M     Trainable params\n",
      "0         Non-trainable params\n",
      "111 M     Total params\n",
      "445.592   Total estimated model params size (MB)\n"
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      "/home/urchade.zaratiana/.conda/envs/py37_UZA/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:433: UserWarning: The number of training samples (44) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
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