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dl_ner_transformer/transformer_pipeline.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"source": [
"## Hugging Face transformer pipeline NER"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"### Testowanie pobranego modelu"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n",
"Using a pipeline without specifying a model name and revision in production is not recommended.\n",
"C:\\Users\\Adrian\\miniconda3\\lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/plain": "config.json: 0%| | 0.00/998 [00:00<?, ?B/s]",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "15443631948a4798b6b504bfb7f55fa7"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Adrian\\miniconda3\\lib\\site-packages\\huggingface_hub\\file_download.py:157: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\Adrian\\.cache\\huggingface\\hub\\models--dbmdz--bert-large-cased-finetuned-conll03-english. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
"To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
" warnings.warn(message)\n"
]
},
{
"data": {
"text/plain": "model.safetensors: 0%| | 0.00/1.33G [00:00<?, ?B/s]",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "6e3eb31e210d40e18eccc2dc37039893"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
"- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
},
{
"data": {
"text/plain": "tokenizer_config.json: 0%| | 0.00/60.0 [00:00<?, ?B/s]",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "592ea7a656c54346b206bf609617f2e4"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": "vocab.txt: 0%| | 0.00/213k [00:00<?, ?B/s]",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "396217db1a9a41358793b72fb709a5b6"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'entity': 'I-ORG', 'score': 0.9995635, 'index': 1, 'word': 'Hu', 'start': 0, 'end': 2}, {'entity': 'I-ORG', 'score': 0.99159384, 'index': 2, 'word': '##gging', 'start': 2, 'end': 7}, {'entity': 'I-ORG', 'score': 0.99826705, 'index': 3, 'word': 'Face', 'start': 8, 'end': 12}, {'entity': 'I-ORG', 'score': 0.9994404, 'index': 4, 'word': 'Inc', 'start': 13, 'end': 16}, {'entity': 'I-LOC', 'score': 0.99943465, 'index': 11, 'word': 'New', 'start': 40, 'end': 43}, {'entity': 'I-LOC', 'score': 0.99932706, 'index': 12, 'word': 'York', 'start': 44, 'end': 48}, {'entity': 'I-LOC', 'score': 0.9993864, 'index': 13, 'word': 'City', 'start': 49, 'end': 53}, {'entity': 'I-LOC', 'score': 0.9825622, 'index': 19, 'word': 'D', 'start': 79, 'end': 80}, {'entity': 'I-LOC', 'score': 0.93698275, 'index': 20, 'word': '##UM', 'start': 80, 'end': 82}, {'entity': 'I-LOC', 'score': 0.89870983, 'index': 21, 'word': '##BO', 'start': 82, 'end': 84}, {'entity': 'I-LOC', 'score': 0.97582406, 'index': 29, 'word': 'Manhattan', 'start': 113, 'end': 122}, {'entity': 'I-LOC', 'score': 0.99024945, 'index': 30, 'word': 'Bridge', 'start': 123, 'end': 129}]\n"
]
}
],
"source": [
"from transformers import pipeline\n",
"\n",
"nlp = pipeline(\"ner\")\n",
"\n",
"sequence = \"Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very\" \\\n",
" \"close to the Manhattan Bridge which is visible from the window.\"\n",
"\n",
"print(nlp(sequence))"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
add data files
2024-05-30 19:07:24 +02:00
"#### Jak widać model inaczej tokenizuje zdania niż przykład z zad. 3, i dane wyjściowe też muszą być zmienione żeby tagi BIO odnosiły się do słów a nie ich części"
Upload files to "/"
2024-05-30 18:55:09 +02:00
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'entity': 'I-ORG', 'score': 0.9995635, 'index': 1, 'word': 'Hu', 'start': 0, 'end': 2}, {'entity': 'I-ORG', 'score': 0.99159384, 'index': 2, 'word': '##gging', 'start': 2, 'end': 7}, {'entity': 'I-ORG', 'score': 0.99826705, 'index': 3, 'word': 'Face', 'start': 8, 'end': 12}, {'entity': 'I-ORG', 'score': 0.9994404, 'index': 4, 'word': 'Inc', 'start': 13, 'end': 16}, {'entity': 'I-LOC', 'score': 0.99943465, 'index': 11, 'word': 'New', 'start': 40, 'end': 43}, {'entity': 'I-LOC', 'score': 0.99932706, 'index': 12, 'word': 'York', 'start': 44, 'end': 48}, {'entity': 'I-LOC', 'score': 0.9993864, 'index': 13, 'word': 'City', 'start': 49, 'end': 53}, {'entity': 'I-LOC', 'score': 0.9825622, 'index': 19, 'word': 'D', 'start': 79, 'end': 80}, {'entity': 'I-LOC', 'score': 0.93698275, 'index': 20, 'word': '##UM', 'start': 80, 'end': 82}, {'entity': 'I-LOC', 'score': 0.89870983, 'index': 21, 'word': '##BO', 'start': 82, 'end': 84}, {'entity': 'I-LOC', 'score': 0.97582406, 'index': 29, 'word': 'Manhattan', 'start': 113, 'end': 122}, {'entity': 'I-LOC', 'score': 0.99024945, 'index': 30, 'word': 'Bridge', 'start': 123, 'end': 129}]\n",
"Bridge\n"
]
}
],
"source": [
"tokenized_raw = nlp(sequence)\n",
"print(tokenized_raw)\n",
"\n",
"print(sequence[123:129])\n",
"\n"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 8,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"456\n",
"456\n"
]
}
],
"source": [
"with open(\"en-ner-conll-2003/dev-0/in.tsv\",\"r\",encoding=\"utf-8\") as f:\n",
" dev_in_lines = f.readlines()\n",
"\n",
"with open(\"en-ner-conll-2003/dev-0/expected.tsv\",\"r\",encoding=\"utf-8\") as f:\n",
" dev_expected_lines = f.readlines()\n",
"\n",
"line_in_split = dev_in_lines[0].split()\n",
"line_expected_split = dev_expected_lines[0].split()\n",
"\n",
"print(len(line_expected_split))\n",
"print(len(line_in_split))"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 11,
"outputs": [],
"source": [
"def generate_date(filename):\n",
" with open(filename, \"r\", encoding=\"utf-8\") as f:\n",
" lines = f.readlines()\n",
" for l in lines:\n",
" yield l"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 12,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn [12], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m out \u001B[38;5;129;01min\u001B[39;00m nlp(generate_date(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124men-ner-conll-2003/dev-0/in.tsv\u001B[39m\u001B[38;5;124m\"\u001B[39m)):\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28mprint\u001B[39m(out)\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\pipelines\\pt_utils.py:124\u001B[0m, in \u001B[0;36mPipelineIterator.__next__\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 121\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mloader_batch_item()\n\u001B[0;32m 123\u001B[0m \u001B[38;5;66;03m# We're out of items within a batch\u001B[39;00m\n\u001B[1;32m--> 124\u001B[0m item \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mnext\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43miterator\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 125\u001B[0m processed \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39minfer(item, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mparams)\n\u001B[0;32m 126\u001B[0m \u001B[38;5;66;03m# We now have a batch of \"inferred things\".\u001B[39;00m\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\pipelines\\pt_utils.py:266\u001B[0m, in \u001B[0;36mPipelinePackIterator.__next__\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 263\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m accumulator\n\u001B[0;32m 265\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m is_last:\n\u001B[1;32m--> 266\u001B[0m processed \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39minfer(\u001B[38;5;28mnext\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39miterator), \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mparams)\n\u001B[0;32m 267\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mloader_batch_size \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 268\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(processed, torch\u001B[38;5;241m.\u001B[39mTensor):\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\pipelines\\base.py:1068\u001B[0m, in \u001B[0;36mPipeline.forward\u001B[1;34m(self, model_inputs, **forward_params)\u001B[0m\n\u001B[0;32m 1066\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m inference_context():\n\u001B[0;32m 1067\u001B[0m model_inputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_ensure_tensor_on_device(model_inputs, device\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdevice)\n\u001B[1;32m-> 1068\u001B[0m model_outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward(model_inputs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mforward_params)\n\u001B[0;32m 1069\u001B[0m model_outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_ensure_tensor_on_device(model_outputs, device\u001B[38;5;241m=\u001B[39mtorch\u001B[38;5;241m.\u001B[39mdevice(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcpu\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n\u001B[0;32m 1070\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\pipelines\\token_classification.py:286\u001B[0m, in \u001B[0;36mTokenClassificationPipeline._forward\u001B[1;34m(self, model_inputs)\u001B[0m\n\u001B[0;32m 284\u001B[0m logits \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodel(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mmodel_inputs)[\u001B[38;5;241m0\u001B[39m]\n\u001B[0;32m 285\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 286\u001B[0m output \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodel(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mmodel_inputs)\n\u001B[0;32m 287\u001B[0m logits \u001B[38;5;241m=\u001B[39m output[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlogits\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(output, \u001B[38;5;28mdict\u001B[39m) \u001B[38;5;28;01melse\u001B[39;00m output[\u001B[38;5;241m0\u001B[39m]\n\u001B[0;32m 289\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m {\n\u001B[0;32m 290\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlogits\u001B[39m\u001B[38;5;124m\"\u001B[39m: logits,\n\u001B[0;32m 291\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mspecial_tokens_mask\u001B[39m\u001B[38;5;124m\"\u001B[39m: special_tokens_mask,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 295\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mmodel_inputs,\n\u001B[0;32m 296\u001B[0m }\n",
"File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\torch\\nn\\modules\\module.py:1130\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m 1126\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m 1127\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m 1128\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m 1129\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1130\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m forward_call(\u001B[38;5;241m*\u001B[39m\u001B[38;5;28minput\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1131\u001B[0m \u001B[38;5;66;03m# Do not call functions when jit is used\u001B[39;00m\n\u001B[0;32m 1132\u001B[0m full_backward_hooks, non_full_backward_hooks \u001B[38;5;241m=\u001B[39m [], []\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:1758\u001B[0m, in \u001B[0;36mBertForTokenClassification.forward\u001B[1;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001B[0m\n\u001B[0;32m 1752\u001B[0m \u001B[38;5;124mr\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 1753\u001B[0m \u001B[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\u001B[39;00m\n\u001B[0;32m 1754\u001B[0m \u001B[38;5;124;03m Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.\u001B[39;00m\n\u001B[0;32m 1755\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 1756\u001B[0m return_dict \u001B[38;5;241m=\u001B[39m return_dict \u001B[38;5;28;01mif\u001B[39;00m return_dict \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mconfig\u001B[38;5;241m.\u001B[39muse_return_dict\n\u001B[1;32m-> 1758\u001B[0m outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbert\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 1759\u001B[0m \u001B[43m \u001B[49m\u001B[43minput_ids\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1760\u001B[0m \u001B[43m \u001B[49m\u001B[43mattention_mask\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mattention_mask\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1761\u001B[0m \u001B[43m \u001B[49m\u001B[43mtoken_type_ids\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtoken_type_ids\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1762\u001B[0m \u001B[43m \u001B[49m\u001B[43mposition_ids\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mposition_ids\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1763\u001B[0m \u001B[43m \u001B[49m\u001B[43mhead_mask\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mhead_mask\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1764\u001B[0m \u001B[43m \u001B[49m\u001B[43minputs_embeds\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minputs_embeds\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1765\u001B[0m \u001B[43m \u001B[49m\u001B[43moutput_attentions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moutput_attentions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1766\u001B[0m \u001B[43m \u001B[49m\u001B[43moutput_hidden_states\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moutput_hidden_states\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1767\u001B[0m \u001B[43m \u001B[49m\u001B[43mreturn_dict\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mreturn_dict\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1768\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1770\u001B[0m sequence_output \u001B[38;5;241m=\u001B[39m outputs[\u001B[38;5;241m0\u001B[39m]\n\u001B[0;32m 1772\u001B[0m sequence_output \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdropout(sequence_output)\n",
"File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\torch\\nn\\modules\\module.py:1130\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m 1126\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m 1127\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m 1128\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m 1129\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1130\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m forward_call(\u001B[38;5;241m*\u001B[39m\u001B[38;5;28minput\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1131\u001B[0m \u001B[38;5;66;03m# Do not call functions when jit is used\u001B[39;00m\n\u001B[0;32m 1132\u001B[0m full_backward_hooks, non_full_backward_hooks \u001B[38;5;241m=\u001B[39m [], []\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:1013\u001B[0m, in \u001B[0;36mBertModel.forward\u001B[1;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001B[0m\n\u001B[0;32m 1004\u001B[0m head_mask \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_head_mask(head_mask, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mconfig\u001B[38;5;241m.\u001B[39mnum_hidden_layers)\n\u001B[0;32m 1006\u001B[0m embedding_output \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39membeddings(\n\u001B[0;32m 1007\u001B[0m input_ids\u001B[38;5;241m=\u001B[39minput_ids,\n\u001B[0;32m 1008\u001B[0m position_ids\u001B[38;5;241m=\u001B[39mposition_ids,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 1011\u001B[0m past_key_values_length\u001B[38;5;241m=\u001B[39mpast_key_values_length,\n\u001B[0;32m 1012\u001B[0m )\n\u001B[1;32m-> 1013\u001B[0m encoder_outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mencoder\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 1014\u001B[0m \u001B[43m \u001B[49m\u001B[43membedding_output\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1015\u001B[0m \u001B[43m \u001B[49m\u001B[43mattention_mask\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mextended_attention_mask\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1016\u001B[0m \u001B[43m \u001B[49m\u001B[43mhead_mask\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mhead_mask\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1017\u001B[0m \u001B[43m \u001B[49m\u001B[43mencoder_hidden_states\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mencoder_hidden_states\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1018\u001B[0m \u001B[43m \u001B[49m\u001B[43mencoder_attention_mask\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mencoder_extended_attention_mask\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1019\u001B[0m \u001B[43m \u001B[49m\u001B[43mpast_key_values\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpast_key_values\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1020\u001B[0m \u001B[43m \u001B[49m\u001B[43muse_cache\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43muse_cache\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1021\u001B[0m \u001B[43m \u001B[49m\u001B[43moutput_attentions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moutput_attentions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1022\u001B[0m \u001B[43m \u001B[49m\u001B[43moutput_hidden_states\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moutput_hidden_states\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1023\u001B[0m \u001B[43m \u001B[49m\u001B[43mreturn_dict\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mreturn_dict\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1024\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1025\u001B[0m sequence_output \u001B[38;5;241m=\u001B[39m encoder_outputs[\u001B[38;5;241m0\u001B[39m]\n\u001B[0;32m 1026\u001B[0m pooled_output \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpooler(sequence_output) \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpooler \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n",
"File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\torch\\nn\\modules\\module.py:1130\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m 1126\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m 1127\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m 1128\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m 1129\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1130\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m forward_call(\u001B[38;5;241m*\u001B[39m\u001B[38;5;28minput\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1131\u001B[0m \u001B[38;5;66;03m# Do not call functions when jit is used\u001B[39;00m\n\u001B[0;32m 1132\u001B[0m full_backward_hooks, non_full_backward_hooks \u001B[38;5;241m=\u001B[39m [], []\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:607\u001B[0m, in \u001B[0;36mBertEncoder.forward\u001B[1;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001B[0m\n\u001B[0;32m 596\u001B[0m layer_outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_gradient_checkpointing_func(\n\u001B[0;32m 597\u001B[0m layer_module\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__call__\u001B[39m,\n\u001B[0;32m 598\u001B[0m hidden_states,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 604\u001B[0m output_attentions,\n\u001B[0;32m 605\u001B[0m )\n\u001B[0;32m 606\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 607\u001B[0m layer_outputs \u001B[38;5;241m=\u001B[39m \u001B[43mlayer_module\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 608\u001B[0m \u001B[43m \u001B[49m\u001B[43mhidden_states\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 609\u001B[0m \u001B[43m \u001B[49m\u001B[43mattention_mask\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 610\u001B[0m \u001B[43m \u001B[49m\u001B[43mlayer_head_mask\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 611\u001B[0m \u001B[43m \u001B[49m\u001B[43mencoder_hidden_states\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 612\u001B[0m \u001B[43m \u001B[49m\u001B[43mencoder_attention_mask\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 613\u001B[0m \u001B[43m \u001B[49m\u001B[43mpast_key_value\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 614\u001B[0m \u001B[43m \u001B[49m\u001B[43moutput_attentions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 615\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 617\u001B[0m hidden_states \u001B[38;5;241m=\u001B[39m layer_outputs[\u001B[38;5;241m0\u001B[39m]\n\u001B[0;32m 618\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m use_cache:\n",
"File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\torch\\nn\\modules\\module.py:1130\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m 1126\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m 1127\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m 1128\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m 1129\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1130\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m forward_call(\u001B[38;5;241m*\u001B[39m\u001B[38;5;28minput\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1131\u001B[0m \u001B[38;5;66;03m# Do not call functions when jit is used\u001B[39;00m\n\u001B[0;32m 1132\u001B[0m full_backward_hooks, non_full_backward_hooks \u001B[38;5;241m=\u001B[39m [], []\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:539\u001B[0m, in \u001B[0;36mBertLayer.forward\u001B[1;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001B[0m\n\u001B[0;32m 536\u001B[0m cross_attn_present_key_value \u001B[38;5;241m=\u001B[39m cross_attention_outputs[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m]\n\u001B[0;32m 537\u001B[0m present_key_value \u001B[38;5;241m=\u001B[39m present_key_value \u001B[38;5;241m+\u001B[39m cross_attn_present_key_value\n\u001B[1;32m--> 539\u001B[0m layer_output \u001B[38;5;241m=\u001B[39m \u001B[43mapply_chunking_to_forward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 540\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfeed_forward_chunk\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mchunk_size_feed_forward\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mseq_len_dim\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mattention_output\u001B[49m\n\u001B[0;32m 541\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 542\u001B[0m outputs \u001B[38;5;241m=\u001B[39m (layer_output,) \u001B[38;5;241m+\u001B[39m outputs\n\u001B[0;32m 544\u001B[0m \u001B[38;5;66;03m# if decoder, return the attn key/values as the last output\u001B[39;00m\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\pytorch_utils.py:236\u001B[0m, in \u001B[0;36mapply_chunking_to_forward\u001B[1;34m(forward_fn, chunk_size, chunk_dim, *input_tensors)\u001B[0m\n\u001B[0;32m 233\u001B[0m \u001B[38;5;66;03m# concatenate output at same dimension\u001B[39;00m\n\u001B[0;32m 234\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m torch\u001B[38;5;241m.\u001B[39mcat(output_chunks, dim\u001B[38;5;241m=\u001B[39mchunk_dim)\n\u001B[1;32m--> 236\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_fn\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43minput_tensors\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:552\u001B[0m, in \u001B[0;36mBertLayer.feed_forward_chunk\u001B[1;34m(self, attention_output)\u001B[0m\n\u001B[0;32m 550\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfeed_forward_chunk\u001B[39m(\u001B[38;5;28mself\u001B[39m, attention_output):\n\u001B[0;32m 551\u001B[0m intermediate_output \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mintermediate(attention_output)\n\u001B[1;32m--> 552\u001B[0m layer_output \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moutput\u001B[49m\u001B[43m(\u001B[49m\u001B[43mintermediate_output\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mattention_output\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 553\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m layer_output\n",
"File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\torch\\nn\\modules\\module.py:1130\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m 1126\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m 1127\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m 1128\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m 1129\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1130\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m forward_call(\u001B[38;5;241m*\u001B[39m\u001B[38;5;28minput\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1131\u001B[0m \u001B[38;5;66;03m# Do not call functions when jit is used\u001B[39;00m\n\u001B[0;32m 1132\u001B[0m full_backward_hooks, non_full_backward_hooks \u001B[38;5;241m=\u001B[39m [], []\n",
"File \u001B[1;32m~\\miniconda3\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:464\u001B[0m, in \u001B[0;36mBertOutput.forward\u001B[1;34m(self, hidden_states, input_tensor)\u001B[0m\n\u001B[0;32m 463\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mforward\u001B[39m(\u001B[38;5;28mself\u001B[39m, hidden_states: torch\u001B[38;5;241m.\u001B[39mTensor, input_tensor: torch\u001B[38;5;241m.\u001B[39mTensor) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m torch\u001B[38;5;241m.\u001B[39mTensor:\n\u001B[1;32m--> 464\u001B[0m hidden_states \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdense\u001B[49m\u001B[43m(\u001B[49m\u001B[43mhidden_states\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 465\u001B[0m hidden_states \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdropout(hidden_states)\n\u001B[0;32m 466\u001B[0m hidden_states \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mLayerNorm(hidden_states \u001B[38;5;241m+\u001B[39m input_tensor)\n",
"File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\torch\\nn\\modules\\module.py:1130\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m 1126\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m 1127\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m 1128\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m 1129\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1130\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m forward_call(\u001B[38;5;241m*\u001B[39m\u001B[38;5;28minput\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1131\u001B[0m \u001B[38;5;66;03m# Do not call functions when jit is used\u001B[39;00m\n\u001B[0;32m 1132\u001B[0m full_backward_hooks, non_full_backward_hooks \u001B[38;5;241m=\u001B[39m [], []\n",
"File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\torch\\nn\\modules\\linear.py:114\u001B[0m, in \u001B[0;36mLinear.forward\u001B[1;34m(self, input)\u001B[0m\n\u001B[0;32m 113\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mforward\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;28minput\u001B[39m: Tensor) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Tensor:\n\u001B[1;32m--> 114\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mF\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlinear\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mweight\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbias\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[1;31mKeyboardInterrupt\u001B[0m: "
]
}
],
"source": [
"for out in nlp(generate_date(\"en-ner-conll-2003/dev-0/in.tsv\")):\n",
" print(out)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 15,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 5, 8, 10, 16, 22]\n",
"[0, 4, 6, 10]\n"
]
}
],
"source": [
"import re\n",
"def get_word_indices(string_to_search):\n",
" pattern = \"\\s\\S\"\n",
" #pattern = \"(\\s\\S)|^\\S\"\n",
" matches = re.finditer(pattern, string_to_search)\n",
" indices = [m.start(0)+1 for m in matches]\n",
" if not string_to_search[0].isspace():\n",
" indices.append(0)\n",
" return sorted(indices)\n",
"\n",
"print(get_word_indices(\"This is a test, test, test\"))\n",
"print(get_word_indices(\"aaa . <S> aaaa\"))"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 46,
"outputs": [],
"source": [
"def get_word_beginning(string_to_search, letter_index):\n",
" current_index = letter_index\n",
" while current_index>0:\n",
" current_index-=1\n",
" if string_to_search[current_index]==\" \":\n",
" return current_index+1\n",
" return 0"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 48,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('NATO', 'I-ORG'),\n",
" ('monitors', 'O'),\n",
" ('Moslem', 'I-MISC'),\n",
" ('move', 'O'),\n",
" ('towards', 'O'),\n",
" ('tense', 'O'),\n",
" ('village', 'O'),\n",
" ('.', 'O'),\n",
" ('</S>', 'O'),\n",
" ('MAHALA', 'I-LOC'),\n",
" (',', 'O'),\n",
" ('Bosnia', 'I-LOC'),\n",
" ('1996-08-31', 'O'),\n",
" ('</S>', 'O'),\n",
" ('NATO', 'I-ORG'),\n",
" ('said', 'O'),\n",
" ('it', 'O'),\n",
" ('was', 'O'),\n",
" ('closely', 'O'),\n",
" ('monitoring', 'O'),\n",
" ('the', 'O'),\n",
" ('movement', 'O'),\n",
" ('of', 'O'),\n",
" ('about', 'O'),\n",
" ('75', 'O'),\n",
" ('Moslem', 'I-MISC'),\n",
" ('men', 'O'),\n",
" ('towards', 'O'),\n",
" ('the', 'O'),\n",
" ('village', 'O'),\n",
" ('of', 'O'),\n",
" ('Mahala', 'I-LOC'),\n",
" ('in', 'O'),\n",
" ('Bosnia', 'I-LOC'),\n",
" (\"'s\", 'O'),\n",
" ('Serb', 'I-MISC'),\n",
" ('republic', 'O'),\n",
" ('on', 'O'),\n",
" ('Saturday', 'O'),\n",
" (',', 'O'),\n",
" ('two', 'O'),\n",
" ('days', 'O'),\n",
" ('after', 'O'),\n",
" ('a', 'O'),\n",
" ('violent', 'O'),\n",
" ('confrontation', 'O'),\n",
" ('with', 'O'),\n",
" ('Serbs', 'I-MISC'),\n",
" ('.', 'O'),\n",
" ('</S>', 'O'),\n",
" ('\"', 'O'),\n",
" ('I', 'O'),\n",
" ('have', 'O'),\n",
" ('to', 'O'),\n",
" ('report', 'O'),\n",
" ('this', 'O'),\n",
" ('morning', 'O'),\n",
" ('that', 'O'),\n",
" ('we', 'O'),\n",
" ('have', 'O'),\n",
" ('in', 'O'),\n",
" ('fact', 'O'),\n",
" ('received', 'O'),\n",
" ('reports', 'O'),\n",
" ('...', 'O'),\n",
" ('</S>', 'O'),\n",
" ('that', 'O'),\n",
" ('up', 'O'),\n",
" ('to', 'O'),\n",
" ('75', 'O'),\n",
" ('Moslem', 'I-MISC'),\n",
" ('men', 'O'),\n",
" ('are', 'O'),\n",
" ('believed', 'O'),\n",
" ('to', 'O'),\n",
" ('be', 'O'),\n",
" ('approaching', 'O'),\n",
" ('Mahala', 'I-LOC'),\n",
" (',', 'O'),\n",
" ('\"', 'O'),\n",
" ('NATO', 'I-ORG'),\n",
" ('spokesman', 'O'),\n",
" ('Lieutenant-Colonel', 'O'),\n",
" ('Max', 'I-PER'),\n",
" ('Marriner', 'I-PER'),\n",
" ('said', 'O'),\n",
" ('in', 'O'),\n",
" ('Sarajevo', 'I-LOC'),\n",
" ('.', 'O'),\n",
" ('</S>', 'O'),\n",
" ('Marriner', 'I-PER'),\n",
" ('said', 'O'),\n",
" ('that', 'O'),\n",
" ('NATO', 'I-ORG'),\n",
" ('troops', 'O'),\n",
" ('had', 'O'),\n",
" ('set', 'O'),\n",
" ('up', 'O'),\n",
" ('a', 'O'),\n",
" ('checkpoint', 'O'),\n",
" ('on', 'O'),\n",
" ('the', 'O'),\n",
" ('road', 'O'),\n",
" ('between', 'O'),\n",
" ('Tuzla', 'I-LOC'),\n",
" ('and', 'O'),\n",
" ('Mahala', 'I-LOC'),\n",
" ('to', 'O'),\n",
" ('establish', 'O'),\n",
" ('the', 'O'),\n",
" ('identities', 'O'),\n",
" ('and', 'O'),\n",
" ('intentions', 'O'),\n",
" ('of', 'O'),\n",
" ('the', 'O'),\n",
" ('men', 'O'),\n",
" ('headed', 'O'),\n",
" ('towards', 'O'),\n",
" ('the', 'O'),\n",
" ('village', 'O'),\n",
" ('.', 'O'),\n",
" ('</S>', 'O'),\n",
" ('Mahala', 'I-LOC'),\n",
" ('is', 'O'),\n",
" ('a', 'O'),\n",
" ('Moslem', 'I-MISC'),\n",
" ('village', 'O'),\n",
" ('on', 'O'),\n",
" ('Bosnian', 'I-MISC'),\n",
" ('Serb', 'I-MISC'),\n",
" ('republic', 'O'),\n",
" ('territory', 'O'),\n",
" ('.', 'O'),\n",
" ('</S>', 'O'),\n",
" ('Moslems', 'I-MISC'),\n",
" ('were', 'O'),\n",
" ('driven', 'O'),\n",
" ('from', 'O'),\n",
" ('the', 'O'),\n",
" ('village', 'O'),\n",
" ('during', 'O'),\n",
" ('the', 'O'),\n",
" ('43-', 'O'),\n",
" ('month', 'O'),\n",
" ('Bosnian', 'I-MISC'),\n",
" ('war', 'O'),\n",
" ('and', 'O'),\n",
" ('most', 'O'),\n",
" ('of', 'O'),\n",
" ('their', 'O'),\n",
" ('houses', 'O'),\n",
" ('were', 'O'),\n",
" ('destroyed', 'O'),\n",
" ('.', 'O'),\n",
" ('</S>', 'O'),\n",
" ('Some', 'O'),\n",
" ('Moslems', 'I-MISC'),\n",
" ('began', 'O'),\n",
" ('returning', 'O'),\n",
" ('to', 'O'),\n",
" ('rebuild', 'O'),\n",
" ('their', 'O'),\n",
" ('properties', 'O'),\n",
" ('earlier', 'O'),\n",
" ('in', 'O'),\n",
" ('the', 'O'),\n",
" ('week', 'O'),\n",
" ('.', 'O'),\n",
" ('</S>', 'O'),\n",
" ('Fights', 'O'),\n",
" ('and', 'O'),\n",
" ('shooting', 'O'),\n",
" ('broke', 'O'),\n",
" ('out', 'O'),\n",
" ('between', 'O'),\n",
" ('the', 'O'),\n",
" ('Moslems', 'I-MISC'),\n",
" ('and', 'O'),\n",
" ('Serb', 'I-MISC'),\n",
" ('police', 'O'),\n",
" ('on', 'O'),\n",
" ('Thursday', 'O'),\n",
" ('and', 'O'),\n",
" ('NATO', 'I-ORG'),\n",
" ('troops', 'O'),\n",
" ('finally', 'O'),\n",
" ('brought', 'O'),\n",
" ('restored', 'O'),\n",
" ('order', 'O'),\n",
" ('.', 'O'),\n",
" ('</S>', 'O'),\n",
" ('A', 'O'),\n",
" ('Reuters', 'I-ORG'),\n",
" ('reporter', 'O'),\n",
" ('who', 'O'),\n",
" ('entered', 'O'),\n",
" ('Mahala', 'I-LOC'),\n",
" ('on', 'O'),\n",
" ('Saturday', 'O'),\n",
" ('morning', 'O'),\n",
" ('found', 'O'),\n",
" ('it', 'O'),\n",
" ('tranquil', 'O'),\n",
" ('but', 'O'),\n",
" ('NATO', 'I-ORG'),\n",
" ('troops', 'O'),\n",
" ('and', 'O'),\n",
" ('U.N.', 'I-ORG'),\n",
" ('police', 'O'),\n",
" ('were', 'O'),\n",
" ('seen', 'O'),\n",
" ('on', 'O'),\n",
" ('the', 'O'),\n",
" ('ground', 'O'),\n",
" ('and', 'O'),\n",
" ('NATO', 'I-ORG'),\n",
" ('helicopters', 'O'),\n",
" ('flew', 'O'),\n",
" ('overhead', 'O'),\n",
" ('.', 'O'),\n",
" ('</S>', 'O')]\n",
"221\n",
"221\n",
"221\n"
]
}
],
"source": [
"from pprint import pprint\n",
"def wordpiece_to_word_tokenization(ner_tokenized, original_sentence):\n",
" # https://stackoverflow.com/a/72685554 # Zmodyfikowana wersja tego, żeby tagi O też dodawać\n",
" #tag_start_indices = set()\n",
" word_start_index_to_tag = dict()\n",
" formatted_results = []\n",
" for result in ner_tokenized:\n",
" end = result[\"start\"]+len(result[\"word\"].replace(\"##\", \"\"))\n",
"\n",
" abbreviation_flag = False # Dodane ze względu na problemy ze skrótem U.N. tokenizowanym jako dwa słowa bez ##\n",
" if len(formatted_results)>0 and not original_sentence[result[\"start\"]-1].isspace():\n",
" abbreviation_flag=True\n",
" if (result[\"word\"].startswith(\"##\") and len(formatted_results)>0 and result[\"start\"]==formatted_results[-1][\"end\"]) or abbreviation_flag:\n",
" formatted_results[-1][\"end\"] = end\n",
" formatted_results[-1][\"word\"]+= result[\"word\"].replace(\"##\", \"\")\n",
" elif len(formatted_results)==0 or (result[\"word\"].startswith(\"##\") and not result[\"start\"] == formatted_results[-1][\"end\"]):\n",
" formatted_results.append({\n",
" 'start': result[\"start\"],\n",
" 'end': end,\n",
" 'entity': result[\"entity\"],\n",
" 'index': result[\"index\"],\n",
" 'score': result[\"score\"],\n",
" 'word': result[\"word\"]})\n",
" formatted_results[-1][\"start\"] = get_word_beginning(original_sentence,formatted_results[-1][\"start\"])\n",
" else:\n",
" formatted_results.append({\n",
" 'start': result[\"start\"],\n",
" 'end': end,\n",
" 'entity': result[\"entity\"],\n",
" 'index': result[\"index\"],\n",
" 'score': result[\"score\"],\n",
" 'word': result[\"word\"]})\n",
" #print(sorted(formatted_results, key=lambda x: x[\"start\"]))\n",
" for result in formatted_results:\n",
" word_start_index_to_tag[result[\"start\"]] = result[\"entity\"]\n",
" for index in get_word_indices(original_sentence):\n",
" if index not in word_start_index_to_tag:\n",
" word_start_index_to_tag[index] = \"O\"\n",
" return [word_start_index_to_tag[index] for index in sorted(word_start_index_to_tag.keys())]\n",
"\n",
"def get_ner_tokenization(sentence):\n",
" wordpiece_tokenized = nlp(sentence)\n",
" final = wordpiece_to_word_tokenization(wordpiece_tokenized, sentence)\n",
" input_word_tokens = sentence.split()\n",
" pprint(list(zip(input_word_tokens, final)))\n",
" return final\n",
"\n",
"test_string= \"\"\"NATO monitors Moslem move towards tense village . </S> MAHALA , Bosnia 1996-08-31 </S> NATO said it was closely monitoring the movement of about 75 Moslem men towards the village of Mahala in Bosnia 's Serb republic on Saturday , two days after a violent confrontation with Serbs . </S> \" I have to report this morning that we have in fact received reports ... </S> that up to 75 Moslem men are believed to be approaching Mahala , \" NATO spokesman Lieutenant-Colonel Max Marriner said in Sarajevo . </S> Marriner said that NATO troops had set up a checkpoint on the road between Tuzla and Mahala to establish the identities and intentions of the men headed towards the village . </S> Mahala is a Moslem village on Bosnian Serb republic territory . </S> Moslems were driven from the village during the 43- month Bosnian war and most of their houses were destroyed . </S> Some Moslems began returning to rebuild their properties earlier in the week . </S> Fights and shooting broke out between the Moslems and Serb police on Thursday and NATO troops finally brought restored order . </S> A Reuters reporter who entered Mahala on Saturday morning found it tranquil but NATO troops and U.N. police were seen on the ground and NATO helicopters flew overhead . </S>\"\"\"\n",
"\n",
"test_tokenized = get_ner_tokenization(test_string)\n",
"expected_out = \"\"\"B-ORG O B-MISC O O O O O O B-LOC O B-LOC O O B-ORG O O O O O O O O O O B-MISC O O O O O B-LOC O B-LOC O B-MISC O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O B-MISC O O O O O O B-LOC O O B-ORG O O B-PER I-PER O O B-LOC O O B-PER O O B-ORG O O O O O O O O O O B-LOC O B-LOC O O O O O O O O O O O O O O O B-LOC O O B-MISC O O B-MISC I-MISC O O O O B-MISC O O O O O O O O O B-MISC O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O B-MISC O B-MISC O O O O B-ORG O O O O O O O O B-ORG O O O B-LOC O O O O O O O B-ORG O O B-ORG O O O O O O O B-ORG O O O O O\"\"\"\n",
"\n",
"print(len(test_tokenized))\n",
"print(len(test_string.split()))\n",
"print(len(expected_out.split()))"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"nlp(\"\"\"CRICKET - ENGLISH COUNTY CHAMPIONSHIP SCORES . </S> LONDON 1996-08-30 </S> Result and close of play scores in English county championship matches on Friday : </S> Leicester : Leicestershire beat Somerset by an innings and 39 runs . </S> Somerset 83 and 174 ( P. Simmons 4-38 ) , Leicestershire 296 . </S> Leicestershire 22 points , Somerset 4 . </S> Chester-le-Street : Glamorgan 259 and 207 ( A. Dale 69 , H. Morris 69 ; D. Blenkiron 4-43 ) , Durham 114 ( S. Watkin 4-28 ) and 81-3 . </S> Tunbridge Wells : Nottinghamshire 214 ( P. Johnson 84 ; M. McCague 4-55 ) , Kent 108-3 . </S> London ( The Oval ) : Warwickshire 195 , Surrey 429-7 ( C. Lewis 80 not out , M. Butcher 70 , G. Kersey 63 , J. Ratcliffe 63 , D. Bicknell 55 ) . </S> Hove : Sussex 363 ( W. Athey 111 , V. Drakes 52 ; I. Austin 4-37 ) , Lancashire 197-8 ( W. Hegg 54 ) </S> Portsmouth : Middlesex 199 and 426 ( J. Pooley 111 , M. Ramprakash 108 , M. Gatting 83 ) , Hampshire 232 and 109-5 . </S> Chesterfield : Worcestershire 238 and 133-5 , Derbyshire 471 ( J. Adams 123 , T.O'Gorman 109 not out , K. Barnett 87 ; T. Moody 6-82 ) </S> Bristol : Gloucestershire 183 and 185-6 ( J. Russell 56 not out ) , Northamptonshire 190 ( K. Curran 52 ; A. Smith 5-68 ) . </S>\"\"\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 43,
"outputs": [
{
"data": {
"text/plain": "[{'entity': 'I-MISC',\n 'score': 0.46102676,\n 'index': 2,\n 'word': '##IC',\n 'start': 2,\n 'end': 4},\n {'entity': 'I-MISC',\n 'score': 0.99607205,\n 'index': 6,\n 'word': 'E',\n 'start': 10,\n 'end': 11},\n {'entity': 'I-MISC',\n 'score': 0.95949733,\n 'index': 7,\n 'word': '##NG',\n 'start': 11,\n 'end': 13},\n {'entity': 'I-MISC',\n 'score': 0.95654744,\n 'index': 8,\n 'word': '##L',\n 'start': 13,\n 'end': 14},\n {'entity': 'I-MISC',\n 'score': 0.50948775,\n 'index': 10,\n 'word': '##H',\n 'start': 16,\n 'end': 17},\n {'entity': 'I-MISC',\n 'score': 0.9255237,\n 'index': 11,\n 'word': 'CO',\n 'start': 18,\n 'end': 20},\n {'entity': 'I-MISC',\n 'score': 0.62757576,\n 'index': 12,\n 'word': '##UN',\n 'start': 20,\n 'end': 22},\n {'entity': 'I-MISC',\n 'score': 0.8929872,\n 'index': 13,\n 'word': '##TY',\n 'start': 22,\n 'end': 24},\n {'entity': 'I-MISC',\n 'score': 0.97692674,\n 'index': 14,\n 'word': 'CH',\n 'start': 25,\n 'end': 27},\n {'entity': 'I-MISC',\n 'score': 0.45881018,\n 'index': 15,\n 'word': '##AM',\n 'start': 27,\n 'end': 29},\n {'entity': 'I-MISC',\n 'score': 0.6647645,\n 'index': 19,\n 'word': '##H',\n 'start': 34,\n 'end': 35},\n {'entity': 'I-LOC',\n 'score': 0.99827445,\n 'index': 29,\n 'word': 'L',\n 'start': 52,\n 'end': 53},\n {'entity': 'I-LOC',\n 'score': 0.98857844,\n 'index': 30,\n 'word': '##ON',\n 'start': 53,\n 'end': 55},\n {'entity': 'I-LOC',\n 'score': 0.8327747,\n 'index': 31,\n 'word': '##D',\n 'start': 55,\n 'end': 56},\n {'entity': 'I-LOC',\n 'score': 0.9980028,\n 'index': 32,\n 'word': '##ON',\n 'start': 56,\n 'end': 58},\n {'entity': 'I-MISC',\n 'score': 0.99895835,\n 'index': 51,\n 'word': 'English',\n 'start': 110,\n 'end': 117},\n {'entity': 'I-LOC',\n 'score': 0.99821544,\n 'index': 62,\n 'word': 'Leicester',\n 'start': 163,\n 'end': 172},\n {'entity': 'I-ORG',\n 'score': 0.99364024,\n 'index': 64,\n 'word': 'Leicestershire',\n 'start': 175,\n 'end': 189},\n {'entity': 'I-ORG',\n 'score': 0.99720687,\n 'index': 66,\n 'word': 'Somerset',\n 'start': 195,\n 'end': 203},\n {'entity': 'I-ORG',\n 'score': 0.9979146,\n 'index': 78,\n 'word': 'Somerset',\n 'start': 237,\n 'end': 245},\n {'entity': 'I-PER',\n 'score': 0.9997062,\n 'index': 83,\n 'word': 'P',\n 'start': 259,\n 'end': 260},\n {'entity': 'I-PER',\n 'score': 0.8936373,\n 'index': 84,\n 'word': '.',\n 'start': 260,\n 'end': 261},\n {'entity': 'I-PER',\n 'score': 0.9998217,\n 'index': 85,\n 'word': 'Simmons',\n 'start': 262,\n 'end': 269},\n {'entity': 'I-ORG',\n 'score': 0.9983664,\n 'index': 91,\n 'word': 'Leicestershire',\n 'start': 279,\n 'end': 293},\n {'entity': 'I-ORG',\n 'score': 0.998063,\n 'index': 99,\n 'word': 'Leicestershire',\n 'start': 305,\n 'end': 319},\n {'entity': 'I-ORG',\n 'score': 0.99934715,\n 'index': 103,\n 'word': 'Somerset',\n 'start': 332,\n 'end': 340},\n {'entity': 'I-LOC',\n 'score': 0.9975303,\n 'index': 110,\n 'word': 'Chester',\n 'start': 350,\n 'end': 357},\n {'entity': 'I-LOC',\n 'score': 0.96212333,\n 'index': 111,\n 'word': '-',\n 'start': 357,\n 'end': 358},\n {'entity': 'I-LOC',\n 'score': 0.9865856,\n 'index': 112,\n 'word': 'le',\n 'start': 358,\n 'end': 360},\n {'entity': 'I-LOC',\n 'score': 0.89539164,\n 'index': 113,\n 'word': '-',\n 'start': 360,\n 'end': 361},\n {'entity': 'I-LOC',\n 'score': 0.98996615,\n 'index': 114,\n 'word': 'Street',\n 'start': 361,\n 'end': 367},\n {'entity': 'I-ORG',\n 'score': 0.9994879,\n 'index': 116,\n 'word': 'Glamorgan',\n 'start': 370,\n 'end': 379},\n {'entity': 'I-PER',\n 'score': 0.99971515,\n 'index': 122,\n 'word': 'A',\n 'start': 394,\n 'end': 395},\n {'entity': 'I-PER',\n 'score': 0.9317695,\n 'index': 123,\n 'word': '.',\n 'start': 395,\n 'end': 396},\n {'entity': 'I-PER',\n 'score': 0.9998431,\n 'index': 124,\n 'word': 'Dale',\n 'start': 397,\n 'end': 401},\n {'entity': 'I-PER',\n 'score': 0.99972874,\n 'index': 127,\n 'word': 'H',\n 'star
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nlp(\"\"\"CRICKET - ENGLISH COUNTY CHAMPIONSHIP SCORES . </S> LONDON 1996-08-30 </S> Result and close of play scores in English county championship matches on Friday : </S> Leicester : Leicestershire beat Somerset by an innings and 39 runs . </S> Somerset 83 and 174 ( P. Simmons 4-38 ) , Leicestershire 296 . </S> Leicestershire 22 points , Somerset 4 . </S> Chester-le-Street : Glamorgan 259 and 207 ( A. Dale 69 , H. Morris 69 ; D. Blenkiron 4-43 ) , Durham 114 ( S. Watkin 4-28 ) and 81-3 . </S> Tunbridge Wells : Nottinghamshire 214 ( P. Johnson 84 ; M. McCague 4-55 ) , Kent 108-3 . </S> London ( The Oval ) : Warwickshire 195 , Surrey 429-7 ( C. Lewis 80 not out , M. Butcher 70 , G. Kersey 63 , J. Ratcliffe 63 , D. Bicknell 55 ) . </S> Hove : Sussex 363 ( W. Athey 111 , V. Drakes 52 ; I. Austin 4-37 ) , Lancashire 197-8 ( W. Hegg 54 ) </S> Portsmouth : Middlesex 199 and 426 ( J. Pooley 111 , M. Ramprakash 108 , M. Gatting 83 ) , Hampshire 232 and 109-5 . </S> Chesterfield : Worcestershire 238 and 133-5 , Derbyshire 471 ( J. Adams 123 , T.O'Gorman 109 not out , K. Barnett 87 ; T. Moody 6-82 ) </S> Bristol : Gloucestershire 183 and 185-6 ( J. Russell 56 not out ) , Northamptonshire 190 ( K. Curran 52 ; A. Smith 5-68 ) . </S>\"\"\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"W wiadomości wyświetlanej przy pobieraniu domyślnego modelu do zadania NER w bibliotece transformers:\n",
"\n",
"*defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english)*\n",
"\n",
"widać iż ta wersja BERT'a była trenowana na wariancie tego konkretnego zbioru danych, więc zdecydowałem się nie dotrenowywać go *(inna kwestia że i tak nie miałbym do tego zasobów)*"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 49,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5/215\n",
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]
}
],
"source": [
"model_outputs = nlp(generate_date(\"en-ner-conll-2003/dev-0/in.tsv\"))\n",
"with open(\"en-ner-conll-2003/dev-0/in.tsv\",\"r\",encoding=\"utf-8\") as f:\n",
" dev_in_lines = f.readlines()\n",
"original_sentences = dev_in_lines\n",
"processed_data = []\n",
"counter = 1\n",
"for model_out, raw_sentence in zip(model_outputs, dev_in_lines):\n",
" to_append = \" \".join(wordpiece_to_word_tokenization(model_out,raw_sentence))\n",
" processed_data.append(to_append)\n",
" if len(to_append.split())!=len(raw_sentence.split()):\n",
" print(f\"Invalid length for sentence #{counter}\")\n",
" print(\"Generated tags\")\n",
" print(to_append)\n",
" print(\"Original sentence:\")\n",
" print(raw_sentence)\n",
" pprint(list(zip(raw_sentence.split(), to_append.split())))\n",
" raise AssertionError\n",
" counter+=1\n",
" if counter%5==0:\n",
" print(f\"{counter}/{len(original_sentences)}\")\n",
"with open(\"en-ner-conll-2003/dev-0/out_raw.tsv\",\"w\",encoding=\"utf-8\") as f:\n",
" for line in processed_data:\n",
" f.write(f\"{line}\\n\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 50,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5/230\n",
"10/230\n",
"15/230\n",
"20/230\n",
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]
}
],
"source": [
"model_outputs = nlp(generate_date(\"en-ner-conll-2003/test-A/in.tsv\"))\n",
"with open(\"en-ner-conll-2003/test-A/in.tsv\",\"r\",encoding=\"utf-8\") as f:\n",
" in_lines = f.readlines()\n",
"original_sentences = in_lines\n",
"processed_data = []\n",
"counter = 1\n",
"for model_out, raw_sentence in zip(model_outputs, original_sentences):\n",
" to_append = \" \".join(wordpiece_to_word_tokenization(model_out,raw_sentence))\n",
" processed_data.append(to_append)\n",
" if len(to_append.split())!=len(raw_sentence.split()):\n",
" print(f\"Invalid length for sentence #{counter}\")\n",
" print(\"Generated tags\")\n",
" print(to_append)\n",
" print(\"Original sentence:\")\n",
" print(raw_sentence)\n",
" pprint(list(zip(raw_sentence.split(), to_append.split())))\n",
" raise AssertionError\n",
" counter+=1\n",
" if counter%5==0:\n",
" print(f\"{counter}/{len(original_sentences)}\")\n",
"with open(\"en-ner-conll-2003/test-A/out_raw.tsv\",\"w\",encoding=\"utf-8\") as f:\n",
" for line in processed_data:\n",
" f.write(f\"{line}\\n\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"### Funkcja do czyszczenia tagów (skopiowana z rozwiązania z zad. 3)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 53,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'O', 'I-MISC', 'I-ORG', 'B-ORG', 'B-MISC', 'B-PER', 'I-LOC', 'I-PER', 'B-LOC'}\n"
]
}
],
"source": [
"tag_set = set()\n",
"with open(\"en-ner-conll-2003/dev-0/expected.tsv\", \"r\", encoding=\"utf-8\") as f:\n",
" lines = f.readlines()\n",
"for line in lines:\n",
" line_split = line.split()\n",
" for tag in line_split:\n",
" if tag not in tag_set:\n",
" tag_set.add(tag)\n",
"print(tag_set)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 54,
"outputs": [],
"source": [
"inter_to_begin_mapping = {\n",
" \"I-LOC\": \"B-LOC\",\n",
" \"I-MISC\": 'B-MISC',\n",
" 'I-ORG': 'B-ORG',\n",
" 'I-PER': 'B-PER'\n",
"}\n",
"begin_to_inter_mapping = {v: k for k, v in inter_to_begin_mapping.items()}"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 55,
"outputs": [],
"source": [
"def fix_tags_in_file(filename, filename_fixed):\n",
" lines_fixed = []\n",
" with open(filename, \"r\", encoding=\"utf-8\") as f:\n",
" lines = f.readlines()\n",
" lines_tokenized = [line.split() for line in lines]\n",
" for line in lines_tokenized:\n",
" line_fixed = []\n",
" for counter, element in enumerate(line):\n",
" if element==\"O\": # O tag can be placed anywhere\n",
" line_fixed.append(element)\n",
" elif element in inter_to_begin_mapping:\n",
" if counter==0: # Beginning of line, can't check previous tag\n",
" line_fixed.append(inter_to_begin_mapping[element])\n",
" else:\n",
" previous_element = line_fixed[counter-1]\n",
" if previous_element==element or previous_element==inter_to_begin_mapping[element]: # Tag was compatible (same inters or compatible B-->I)\n",
" line_fixed.append(element)\n",
" elif previous_element==\"O\": # O--> Inter\n",
" line_fixed.append(inter_to_begin_mapping[element])\n",
" elif previous_element in inter_to_begin_mapping and element in inter_to_begin_mapping and previous_element!=element: # Incompatible subsequent inter-tags\n",
" line_fixed.append(previous_element)\n",
" else: # Begin --> Incompatible inter\n",
" corrected_tag = begin_to_inter_mapping[previous_element]\n",
" line_fixed.append(corrected_tag)\n",
" elif element in begin_to_inter_mapping: # Beginning tag can be added safely\n",
" line_fixed.append(element)\n",
" else:\n",
" print(\"This shouldn't happen\")\n",
" lines_fixed.append(\" \".join(line_fixed))\n",
" with open(filename_fixed, \"w\", encoding=\"utf-8\") as f:\n",
" for line in lines_fixed:\n",
" f.write(f\"{line}\\n\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 56,
"outputs": [],
"source": [
"fix_tags_in_file(\"en-ner-conll-2003/dev-0/out_raw.tsv\",\"en-ner-conll-2003/dev-0/out.tsv\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 57,
"outputs": [],
"source": [
"fix_tags_in_file(\"en-ner-conll-2003/test-A/out_raw.tsv\",\"en-ner-conll-2003/test-A/out.tsv\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
Reference in New Issue Copy Permalink