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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 17, | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 331, | |
"referenced_widgets": [ | |
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"3ba4804f718d43a4bc6091cddfebc644", | |
"1a28a3fe53e043a7aeb17a4d4214fed0" | |
] | |
}, | |
"id": "tiVVYhpl7kx1", | |
"outputId": "73473b56-f1d9-4bfb-c693-11127a9a001e" | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Token is valid.\n", | |
"Your token has been saved in your configured git credential helpers (store).\n", | |
"Your token has been saved to /root/.cache/huggingface/token\n", | |
"Login successful\n" | |
] | |
} | |
], | |
"source": [ | |
"# login your huggingface account and get token\n", | |
"from huggingface_hub import notebook_login\n", | |
"notebook_login()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 18, | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 158, | |
"referenced_widgets": [ | |
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"id": "keJqlVVbzISW", | |
"outputId": "6493c825-1b7e-4537-8381-2f3183e90c6e" | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"application/vnd.jupyter.widget-view+json": { | |
"model_id": "3e18b80402884af2af5acdba7115bf80", | |
"version_major": 2, | |
"version_minor": 0 | |
}, | |
"text/plain": [ | |
"Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/263M [00:00<?, ?B/s]" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"from transformers import DistilBertForTokenClassification\n", | |
"model = DistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(unique_tags))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 19, | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 1000 | |
}, | |
"id": "TzfPAoKUM2T2", | |
"outputId": "2d2eb77c-6137-493e-f5fb-5e90b134d1ca" | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", | |
" warnings.warn(\n", | |
"***** Running training *****\n", | |
" Num examples = 3719\n", | |
" Num Epochs = 3\n", | |
" Instantaneous batch size per device = 16\n", | |
" Total train batch size (w. parallel, distributed & accumulation) = 16\n", | |
" Gradient Accumulation steps = 1\n", | |
" Total optimization steps = 699\n", | |
" Number of trainable parameters = 65206292\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/html": [ | |
"\n", | |
" <div>\n", | |
" \n", | |
" <progress value='699' max='699' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
" [699/699 02:16, Epoch 3/3]\n", | |
" </div>\n", | |
" <table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: left;\">\n", | |
" <th>Step</th>\n", | |
" <th>Training Loss</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <td>10</td>\n", | |
" <td>3.056700</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>20</td>\n", | |
" <td>2.987800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>30</td>\n", | |
" <td>2.815300</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>40</td>\n", | |
" <td>2.503500</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>50</td>\n", | |
" <td>2.050000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>60</td>\n", | |
" <td>1.232500</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>70</td>\n", | |
" <td>0.623100</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>80</td>\n", | |
" <td>0.530700</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>90</td>\n", | |
" <td>0.410600</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>100</td>\n", | |
" <td>0.317600</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>110</td>\n", | |
" <td>0.331300</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>120</td>\n", | |
" <td>0.377800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>130</td>\n", | |
" <td>0.288000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>140</td>\n", | |
" <td>0.238300</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>150</td>\n", | |
" <td>0.231200</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>160</td>\n", | |
" <td>0.279400</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>170</td>\n", | |
" <td>0.277800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>180</td>\n", | |
" <td>0.185100</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>190</td>\n", | |
" <td>0.278600</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>200</td>\n", | |
" <td>0.298700</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>210</td>\n", | |
" <td>0.204900</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>220</td>\n", | |
" <td>0.160700</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>230</td>\n", | |
" <td>0.205300</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>240</td>\n", | |
" <td>0.140500</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>250</td>\n", | |
" <td>0.165900</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>260</td>\n", | |
" <td>0.140100</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>270</td>\n", | |
" <td>0.177400</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>280</td>\n", | |
" <td>0.112000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>290</td>\n", | |
" <td>0.177900</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>300</td>\n", | |
" <td>0.150500</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>310</td>\n", | |
" <td>0.136800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>320</td>\n", | |
" <td>0.176400</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>330</td>\n", | |
" <td>0.145600</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>340</td>\n", | |
" <td>0.198800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>350</td>\n", | |
" <td>0.157000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>360</td>\n", | |
" <td>0.136300</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>370</td>\n", | |
" <td>0.188800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>380</td>\n", | |
" <td>0.161900</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>390</td>\n", | |
" <td>0.111100</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>400</td>\n", | |
" <td>0.191200</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>410</td>\n", | |
" <td>0.103900</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>420</td>\n", | |
" <td>0.212300</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>430</td>\n", | |
" <td>0.191400</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>440</td>\n", | |
" <td>0.219800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>450</td>\n", | |
" <td>0.197200</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>460</td>\n", | |
" <td>0.173600</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>470</td>\n", | |
" <td>0.161900</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>480</td>\n", | |
" <td>0.106100</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>490</td>\n", | |
" <td>0.091400</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>500</td>\n", | |
" <td>0.144000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>510</td>\n", | |
" <td>0.104500</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>520</td>\n", | |
" <td>0.092000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>530</td>\n", | |
" <td>0.102200</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>540</td>\n", | |
" <td>0.089400</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>550</td>\n", | |
" <td>0.117000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>560</td>\n", | |
" <td>0.159300</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>570</td>\n", | |
" <td>0.135900</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>580</td>\n", | |
" <td>0.065200</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>590</td>\n", | |
" <td>0.095800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>600</td>\n", | |
" <td>0.125600</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>610</td>\n", | |
" <td>0.087500</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>620</td>\n", | |
" <td>0.065800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>630</td>\n", | |
" <td>0.054000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>640</td>\n", | |
" <td>0.098200</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>650</td>\n", | |
" <td>0.092800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>660</td>\n", | |
" <td>0.115200</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>670</td>\n", | |
" <td>0.063800</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>680</td>\n", | |
" <td>0.086200</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <td>690</td>\n", | |
" <td>0.079600</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table><p>" | |
], | |
"text/plain": [ | |
"<IPython.core.display.HTML object>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"Saving model checkpoint to your_model_name/checkpoint-500\n", | |
"Configuration saved in your_model_name/checkpoint-500/config.json\n", | |
"Model weights saved in your_model_name/checkpoint-500/pytorch_model.bin\n", | |
"\n", | |
"\n", | |
"Training completed. Do not forget to share your model on huggingface.co/models =)\n", | |
"\n", | |
"\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"TrainOutput(global_step=699, training_loss=0.37280961048279027, metrics={'train_runtime': 139.3039, 'train_samples_per_second': 80.091, 'train_steps_per_second': 5.018, 'total_flos': 395870659402824.0, 'train_loss': 0.37280961048279027, 'epoch': 3.0})" | |
] | |
}, | |
"execution_count": 20, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer\n", | |
"\n", | |
"model_name = f'your_model_name'\n", | |
"\n", | |
"training_args = TrainingArguments(\n", | |
" model_name,\n", | |
" num_train_epochs=3, # total number of training epochs\n", | |
" per_device_train_batch_size=16, # batch size per device during training\n", | |
" per_device_eval_batch_size=64, # batch size for evaluation\n", | |
" warmup_steps=500, # number of warmup steps for learning rate scheduler\n", | |
" weight_decay=0.01, # strength of weight decay\n", | |
" logging_dir='./logs', # directory for storing logs\n", | |
" logging_steps=10,\n", | |
")\n", | |
"\n", | |
"trainer = Trainer(\n", | |
" model=model, # the instantiated 🤗 Transformers model to be trained\n", | |
" args=training_args, # training arguments, defined above\n", | |
" train_dataset=train_dataset, # training dataset\n", | |
" eval_dataset=val_dataset # evaluation dataset\n", | |
")\n", | |
"\n", | |
"trainer.train()" | |
] | |
} | |
], | |
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"gpuClass": "standard", | |
"kernelspec": { | |
"display_name": "Python 3", | |
"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": "3.8.6" | |
}, | |
"widgets": { | |
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