37 KiB
37 KiB
import pandas as pd
import lzma
from transformers import pipeline
with lzma.open("train/train.tsv.xz", "rt") as f:
train_data = pd.read_csv(f, delimiter='\t', header=None)
in_data_dev0 = pd.read_csv('dev-0/in.tsv', delimiter='\t', header=None)
expected_data_dev0 = pd.read_csv('dev-0/expected.tsv', delimiter='\t', header=None)
in_data_testA = pd.read_csv('dev-0/in.tsv', delimiter='\t', header=None)
expected_data_testA = pd.read_csv('dev-0/expected.tsv', delimiter='\t', header=None)
# Załadowanie pipeline do NER
ner_pipeline = pipeline("ner", grouped_entities=True)
# Przetworzenie danych z in.tsv przy użyciu pipeline
ner_results_dev0 = ner_pipeline(in_data_dev0[0].tolist())
ner_results_testA = ner_pipeline(in_data_testA[0].tolist())
# Wyświetlenie przykładowych wyników
ner_results_dev0[:5]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). Using a pipeline without specifying a model name and revision in production is not recommended. /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`. warnings.warn( 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'] - 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). - 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). /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. warnings.warn(
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'end': 1586},
{'entity_group': 'ORG',
'score': 0.9956975,
'word': 'Glamorgan',
'start': 1605,
'end': 1614}],
[{'entity_group': 'PER',
'score': 0.64593357,
'word': 'SHEARER',
'start': 9,
'end': 16},
{'entity_group': 'LOC',
'score': 0.9937872,
'word': 'ENGLAND',
'start': 26,
'end': 33},
{'entity_group': 'LOC',
'score': 0.98532915,
'word': 'LONDON',
'start': 49,
'end': 55},
{'entity_group': 'PER',
'score': 0.99965554,
'word': 'Alan Shearer',
'start': 106,
'end': 118},
{'entity_group': 'LOC',
'score': 0.99965096,
'word': 'England',
'start': 140,
'end': 147},
{'entity_group': 'ORG',
'score': 0.99851114,
'word': 'Newcastle',
'start': 202,
'end': 211},
{'entity_group': 'PER',
'score': 0.99972355,
'word': 'Tony Adams',
'start': 280,
'end': 290},
{'entity_group': 'MISC',
'score': 0.99899167,
'word': 'European',
'start': 321,
'end': 329},
{'entity_group': 'PER',
'score': 0.99968123,
'word': 'David Platt',
'start': 372,
'end': 383},
{'entity_group': 'PER',
'score': 0.99977285,
'word': 'Adams',
'start': 391,
'end': 396},
{'entity_group': 'PER',
'score': 0.99800897,
'word': 'Platt',
'start': 401,
'end': 406},
{'entity_group': 'LOC',
'score': 0.99970406,
'word': 'England',
'start': 438,
'end': 445},
{'entity_group': 'MISC',
'score': 0.99456084,
'word': 'World Cup',
'start': 457,
'end': 466},
{'entity_group': 'LOC',
'score': 0.9998385,
'word': 'Moldova',
'start': 485,
'end': 492},
{'entity_group': 'PER',
'score': 0.9992155,
'word': 'Shearer',
'start': 510,
'end': 517},
{'entity_group': 'PER',
'score': 0.9990761,
'word': 'Glenn Hoddle',
'start': 571,
'end': 583},
{'entity_group': 'ORG',
'score': 0.9991636,
'word': 'Blackburn',
'start': 621,
'end': 630},
{'entity_group': 'ORG',
'score': 0.99904674,
'word': 'Southampton',
'start': 635,
'end': 646},
{'entity_group': 'PER',
'score': 0.9996246,
'word': 'Alan',
'start': 745,
'end': 749},
{'entity_group': 'PER',
'score': 0.9964572,
'word': 'Hoddle',
'start': 777,
'end': 783},
{'entity_group': 'PER',
'score': 0.9996996,
'word': 'Alan',
'start': 873,
'end': 877},
{'entity_group': 'PER',
'score': 0.99759614,
'word': 'Shearer',
'start': 1172,
'end': 1179},
{'entity_group': 'MISC',
'score': 0.9978334,
'word': 'Euro 96',
'start': 1183,
'end': 1190},
{'entity_group': 'PER',
'score': 0.99211884,
'word': 'Teddy Sheringham',
'start': 1208,
'end': 1224},
{'entity_group': 'PER',
'score': 0.99672574,
'word': 'Shearer',
'start': 1313,
'end': 1320},
{'entity_group': 'ORG',
'score': 0.9980933,
'word': 'Newcastle',
'start': 1324,
'end': 1333},
{'entity_group': 'PER',
'score': 0.9995302,
'word': 'Les Ferdinand',
'start': 1344,
'end': 1357}],
[{'entity_group': 'MISC',
'score': 0.6943369,
'word': 'IN',
'start': 13,
'end': 15},
{'entity_group': 'LOC',
'score': 0.9792612,
'word': 'BELGRADE',
'start': 52,
'end': 60},
{'entity_group': 'MISC',
'score': 0.6702504,
'word': 'S',
'start': 106,
'end': 107},
{'entity_group': 'ORG',
'score': 0.9988783,
'word': 'Red Star',
'start': 148,
'end': 156},
{'entity_group': 'LOC',
'score': 0.99980396,
'word': 'Yugoslavia',
'start': 159,
'end': 169},
{'entity_group': 'ORG',
'score': 0.99583733,
'word': 'Dinamo',
'start': 177,
'end': 183},
{'entity_group': 'LOC',
'score': 0.99982125,
'word': 'Russia',
'start': 186,
'end': 192}]]# Funkcja do mapowania wyników NER na format B-XXX, I-XXX, O
def map_ner_results(ner_results, sentences):
ner_labels = []
for sentence, entities in zip(sentences, ner_results):
words = sentence.split()
labels = ['O'] * len(words)
for entity in entities:
start_idx = entity['start']
end_idx = entity['end']
entity_label = entity['entity_group']
entity_words = sentence[start_idx:end_idx].split()
start_word_idx = len(sentence[:start_idx].split())
end_word_idx = start_word_idx + len(entity_words)
if start_word_idx < len(labels) and end_word_idx <= len(labels):
labels[start_word_idx] = f'B-{entity_label}'
for i in range(start_word_idx + 1, end_word_idx):
labels[i] = f'I-{entity_label}'
ner_labels.append(labels)
return ner_labels
predicted_labels_dev0 = map_ner_results(ner_results_dev0, in_data_dev0[0].tolist())
predicted_labels_testA = map_ner_results(ner_results_testA, in_data_dev0[0].tolist())
predicted_strings_dev0 = [' '.join(labels) for labels in predicted_labels_dev0]
predicted_strings_testA = [' '.join(labels) for labels in predicted_labels_testA]
expected_strings_dev0 = expected_data_dev0[0].tolist()
with open('dev-0/out.tsv', 'w') as f:
for line in predicted_strings_dev0:
f.write(line + '\n')
with open('test-A/out.tsv', 'w') as f:
for line in predicted_strings_testA:
f.write(line + '\n')
# Sprawdzenie zgodności wyników
correct = 0
total = 0
for pred, exp in zip(predicted_strings_dev0, expected_strings_dev0):
pred_labels = pred.split()
exp_labels = exp.split()
for p, e in zip(pred_labels, exp_labels):
if p == e:
correct += 1
total += 1
accuracy = correct / total
print(f"Accuracy - dev-0: {accuracy:.2%}")Accuracy - dev-0: 94.88%