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Implementation transofrmer ner
2024-06-06 22:10:16 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import lzma\n",
"from transformers import pipeline\n",
"\n",
"with lzma.open(\"train/train.tsv.xz\", \"rt\") as f:\n",
" train_data = pd.read_csv(f, delimiter='\\t', header=None)\n",
"\n",
"in_data_dev0 = pd.read_csv('dev-0/in.tsv', delimiter='\\t', header=None)\n",
"expected_data_dev0 = pd.read_csv('dev-0/expected.tsv', delimiter='\\t', header=None)\n",
"\n",
"in_data_testA = pd.read_csv('dev-0/in.tsv', delimiter='\\t', header=None)\n",
"expected_data_testA = pd.read_csv('dev-0/expected.tsv', delimiter='\\t', header=None)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"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",
"/home/wmi/miniconda3/envs/pbr/lib/python3.12/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",
"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",
"/home/wmi/miniconda3/envs/pbr/lib/python3.12/site-packages/transformers/pipelines/token_classification.py:168: UserWarning: `grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to `aggregation_strategy=\"AggregationStrategy.SIMPLE\"` instead.\n",
" warnings.warn(\n"
]
},
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" 'score': 0.99974364,\n",
" 'word': 'England',\n",
" 'start': 128,\n",
" 'end': 135},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9373318,\n",
" 'word': 'Test and County Cricket Board',\n",
" 'start': 201,\n",
" 'end': 230},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.9997092,\n",
" 'word': 'Australia',\n",
" 'start': 258,\n",
" 'end': 267},\n",
" {'entity_group': 'MISC',\n",
" 'score': 0.52167755,\n",
" 'word': 'S',\n",
" 'start': 440,\n",
" 'end': 441},\n",
" {'entity_group': 'MISC',\n",
" 'score': 0.99905914,\n",
" 'word': 'English',\n",
" 'start': 443,\n",
" 'end': 450},\n",
" {'entity_group': 'MISC',\n",
" 'score': 0.9493715,\n",
" 'word': 'British',\n",
" 'start': 484,\n",
" 'end': 491},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.66527855,\n",
" 'word': 'Minor Counties',\n",
" 'start': 551,\n",
" 'end': 565},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.7435069,\n",
" 'word': 'S',\n",
" 'start': 572,\n",
" 'end': 573},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.9995771,\n",
" 'word': 'Scotland',\n",
" 'start': 575,\n",
" 'end': 583},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.99926525,\n",
" 'word': 'London',\n",
" 'start': 639,\n",
" 'end': 645},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.97566634,\n",
" 'word': \"Lord ' s\",\n",
" 'start': 670,\n",
" 'end': 677},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.98920923,\n",
" 'word': \"Duke of Norfolk ' s XI\",\n",
" 'start': 692,\n",
" 'end': 713},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.98959255,\n",
" 'word': 'Arundel',\n",
" 'start': 719,\n",
" 'end': 726},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9956369,\n",
" 'word': 'Northampton',\n",
" 'start': 743,\n",
" 'end': 754},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9987895,\n",
" 'word': 'Worcestershire',\n",
" 'start': 769,\n",
" 'end': 783},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9977308,\n",
" 'word': 'Durham',\n",
" 'start': 798,\n",
" 'end': 804},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.99277717,\n",
" 'word': 'Headingley',\n",
" 'start': 850,\n",
" 'end': 860},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.93061477,\n",
" 'word': 'Leeds',\n",
" 'start': 868,\n",
" 'end': 873},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.9936111,\n",
" 'word': 'The Oval',\n",
" 'start': 922,\n",
" 'end': 930},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.9980288,\n",
" 'word': 'London',\n",
" 'start': 938,\n",
" 'end': 944},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.9947086,\n",
" 'word': \"Lord ' s\",\n",
" 'start': 992,\n",
" 'end': 999},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.99907875,\n",
" 'word': 'London',\n",
" 'start': 1002,\n",
" 'end': 1008},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9987871,\n",
" 'word': 'Gloucestershire',\n",
" 'start': 1028,\n",
" 'end': 1043},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9982962,\n",
" 'word': 'Sussex',\n",
" 'start': 1047,\n",
" 'end': 1053},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9983968,\n",
" 'word': 'Surrey',\n",
" 'start': 1057,\n",
" 'end': 1063},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9983108,\n",
" 'word': 'Derbyshire',\n",
" 'start': 1107,\n",
" 'end': 1117},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.9901608,\n",
" 'word': 'Edgbaston',\n",
" 'start': 1179,\n",
" 'end': 1188},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.99937767,\n",
" 'word': 'Birmingham',\n",
" 'start': 1191,\n",
" 'end': 1201},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.5605512,\n",
" 'word': 'S',\n",
" 'start': 1265,\n",
" 'end': 1266},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9908428,\n",
" 'word': 'Leicestershire',\n",
" 'start': 1281,\n",
" 'end': 1295},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.7768232,\n",
" 'word': 'Lord',\n",
" 'start': 1344,\n",
" 'end': 1348},\n",
" {'entity_group': 'MISC',\n",
" 'score': 0.6473316,\n",
" 'word': 'British',\n",
" 'start': 1372,\n",
" 'end': 1379},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9781204,\n",
" 'word': 'Oxford',\n",
" 'start': 1398,\n",
" 'end': 1404},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.99740857,\n",
" 'word': 'Hampshire',\n",
" 'start': 1438,\n",
" 'end': 1447},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.93818957,\n",
" 'word': 'Old',\n",
" 'start': 1503,\n",
" 'end': 1506},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.60427374,\n",
" 'word': '##rafford',\n",
" 'start': 1508,\n",
" 'end': 1515},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.88743263,\n",
" 'word': 'Manchester',\n",
" 'start': 1518,\n",
" 'end': 1528},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.6398575,\n",
" 'word': 'Minor Counties XI',\n",
" 'start': 1545,\n",
" 'end': 1562},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.9948643,\n",
" 'word': 'Scotland',\n",
" 'start': 1578,\n",
" 'end': 1586},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9956975,\n",
" 'word': 'Glamorgan',\n",
" 'start': 1605,\n",
" 'end': 1614}],\n",
" [{'entity_group': 'PER',\n",
" 'score': 0.64593357,\n",
" 'word': 'SHEARER',\n",
" 'start': 9,\n",
" 'end': 16},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.9937872,\n",
" 'word': 'ENGLAND',\n",
" 'start': 26,\n",
" 'end': 33},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.98532915,\n",
" 'word': 'LONDON',\n",
" 'start': 49,\n",
" 'end': 55},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.99965554,\n",
" 'word': 'Alan Shearer',\n",
" 'start': 106,\n",
" 'end': 118},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.99965096,\n",
" 'word': 'England',\n",
" 'start': 140,\n",
" 'end': 147},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.99851114,\n",
" 'word': 'Newcastle',\n",
" 'start': 202,\n",
" 'end': 211},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.99972355,\n",
" 'word': 'Tony Adams',\n",
" 'start': 280,\n",
" 'end': 290},\n",
" {'entity_group': 'MISC',\n",
" 'score': 0.99899167,\n",
" 'word': 'European',\n",
" 'start': 321,\n",
" 'end': 329},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.99968123,\n",
" 'word': 'David Platt',\n",
" 'start': 372,\n",
" 'end': 383},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.99977285,\n",
" 'word': 'Adams',\n",
" 'start': 391,\n",
" 'end': 396},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.99800897,\n",
" 'word': 'Platt',\n",
" 'start': 401,\n",
" 'end': 406},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.99970406,\n",
" 'word': 'England',\n",
" 'start': 438,\n",
" 'end': 445},\n",
" {'entity_group': 'MISC',\n",
" 'score': 0.99456084,\n",
" 'word': 'World Cup',\n",
" 'start': 457,\n",
" 'end': 466},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.9998385,\n",
" 'word': 'Moldova',\n",
" 'start': 485,\n",
" 'end': 492},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.9992155,\n",
" 'word': 'Shearer',\n",
" 'start': 510,\n",
" 'end': 517},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.9990761,\n",
" 'word': 'Glenn Hoddle',\n",
" 'start': 571,\n",
" 'end': 583},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9991636,\n",
" 'word': 'Blackburn',\n",
" 'start': 621,\n",
" 'end': 630},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.99904674,\n",
" 'word': 'Southampton',\n",
" 'start': 635,\n",
" 'end': 646},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.9996246,\n",
" 'word': 'Alan',\n",
" 'start': 745,\n",
" 'end': 749},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.9964572,\n",
" 'word': 'Hoddle',\n",
" 'start': 777,\n",
" 'end': 783},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.9996996,\n",
" 'word': 'Alan',\n",
" 'start': 873,\n",
" 'end': 877},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.99759614,\n",
" 'word': 'Shearer',\n",
" 'start': 1172,\n",
" 'end': 1179},\n",
" {'entity_group': 'MISC',\n",
" 'score': 0.9978334,\n",
" 'word': 'Euro 96',\n",
" 'start': 1183,\n",
" 'end': 1190},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.99211884,\n",
" 'word': 'Teddy Sheringham',\n",
" 'start': 1208,\n",
" 'end': 1224},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.99672574,\n",
" 'word': 'Shearer',\n",
" 'start': 1313,\n",
" 'end': 1320},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9980933,\n",
" 'word': 'Newcastle',\n",
" 'start': 1324,\n",
" 'end': 1333},\n",
" {'entity_group': 'PER',\n",
" 'score': 0.9995302,\n",
" 'word': 'Les Ferdinand',\n",
" 'start': 1344,\n",
" 'end': 1357}],\n",
" [{'entity_group': 'MISC',\n",
" 'score': 0.6943369,\n",
" 'word': 'IN',\n",
" 'start': 13,\n",
" 'end': 15},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.9792612,\n",
" 'word': 'BELGRADE',\n",
" 'start': 52,\n",
" 'end': 60},\n",
" {'entity_group': 'MISC',\n",
" 'score': 0.6702504,\n",
" 'word': 'S',\n",
" 'start': 106,\n",
" 'end': 107},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.9988783,\n",
" 'word': 'Red Star',\n",
" 'start': 148,\n",
" 'end': 156},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.99980396,\n",
" 'word': 'Yugoslavia',\n",
" 'start': 159,\n",
" 'end': 169},\n",
" {'entity_group': 'ORG',\n",
" 'score': 0.99583733,\n",
" 'word': 'Dinamo',\n",
" 'start': 177,\n",
" 'end': 183},\n",
" {'entity_group': 'LOC',\n",
" 'score': 0.99982125,\n",
" 'word': 'Russia',\n",
" 'start': 186,\n",
" 'end': 192}]]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Załadowanie pipeline do NER\n",
"ner_pipeline = pipeline(\"ner\", grouped_entities=True)\n",
"\n",
"# Przetworzenie danych z in.tsv przy użyciu pipeline\n",
"ner_results_dev0 = ner_pipeline(in_data_dev0[0].tolist())\n",
"ner_results_testA = ner_pipeline(in_data_testA[0].tolist())\n",
"\n",
"# Wyświetlenie przykładowych wyników\n",
"ner_results_dev0[:5]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy - dev-0: 94.88%\n"
]
}
],
"source": [
"# Funkcja do mapowania wyników NER na format B-XXX, I-XXX, O\n",
"def map_ner_results(ner_results, sentences):\n",
" ner_labels = []\n",
"\n",
" for sentence, entities in zip(sentences, ner_results):\n",
" words = sentence.split()\n",
" labels = ['O'] * len(words)\n",
"\n",
" for entity in entities:\n",
" start_idx = entity['start']\n",
" end_idx = entity['end']\n",
" entity_label = entity['entity_group']\n",
" entity_words = sentence[start_idx:end_idx].split()\n",
" start_word_idx = len(sentence[:start_idx].split())\n",
" end_word_idx = start_word_idx + len(entity_words)\n",
"\n",
" if start_word_idx < len(labels) and end_word_idx <= len(labels):\n",
" labels[start_word_idx] = f'B-{entity_label}'\n",
"\n",
" for i in range(start_word_idx + 1, end_word_idx):\n",
" labels[i] = f'I-{entity_label}'\n",
"\n",
" ner_labels.append(labels)\n",
" return ner_labels\n",
"\n",
"predicted_labels_dev0 = map_ner_results(ner_results_dev0, in_data_dev0[0].tolist())\n",
"predicted_labels_testA = map_ner_results(ner_results_testA, in_data_dev0[0].tolist())\n",
"\n",
"predicted_strings_dev0 = [' '.join(labels) for labels in predicted_labels_dev0]\n",
"predicted_strings_testA = [' '.join(labels) for labels in predicted_labels_testA]\n",
"expected_strings_dev0 = expected_data_dev0[0].tolist()\n",
"\n",
"with open('dev-0/out.tsv', 'w') as f:\n",
" for line in predicted_strings_dev0:\n",
" f.write(line + '\\n')\n",
"\n",
"with open('test-A/out.tsv', 'w') as f:\n",
" for line in predicted_strings_testA:\n",
" f.write(line + '\\n')\n",
"\n",
"# Sprawdzenie zgodności wyników\n",
"correct = 0\n",
"total = 0\n",
"for pred, exp in zip(predicted_strings_dev0, expected_strings_dev0):\n",
" pred_labels = pred.split()\n",
" exp_labels = exp.split()\n",
" for p, e in zip(pred_labels, exp_labels):\n",
" if p == e:\n",
" correct += 1\n",
" total += 1\n",
"\n",
"accuracy = correct / total\n",
"print(f\"Accuracy - dev-0: {accuracy:.2%}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Reference in New Issue Copy Permalink