Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
License:
scoring scripts
Browse files
scoring-scripts/compute_MCC.py
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from sklearn.metrics import matthews_corrcoef
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import numpy as np
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def compute_MCC(references_dataset, predictions_dataset, ref_col='ner_tags', pred_col='pred_ner_tags'):
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# computes the Matthews correlation coeff between two datasets
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# sort by id
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references_dataset = references_dataset.sort('unique_id')
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predictions_dataset = predictions_dataset.sort('unique_id')
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# check that tokens match
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assert(references_dataset['tokens']==predictions_dataset['tokens'])
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# the lists have to be flattened
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flat_ref_tags = np.concatenate(references_dataset[ref_col])
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flat_pred_tags = np.concatenate(predictions_dataset[pred_col])
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mcc_score = matthews_corrcoef(y_true=flat_ref_tags,
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y_pred=flat_pred_tags)
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return(mcc_score)
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scoring-scripts/compute_seqeval.py
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from datasets import load_metric
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from ast import literal_eval
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def compute_seqeval(references_dataset, predictions_dataset, ref_col='ner_tags', pred_col='pred_ner_tags'):
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# computes the seqeval scores
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# sort by id
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references_dataset = references_dataset.sort('unique_id')
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predictions_dataset = predictions_dataset.sort('unique_id')
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# load the huggingface metric function
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seqeval = load_metric('seqeval')
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# check that tokens match
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assert(references_dataset['tokens']==predictions_dataset['tokens'])
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# ensure IOB2?
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# compute scores
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seqeval_results = seqeval.compute(predictions = predictions_dataset[pred_col],
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references = references_dataset[ref_col],
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scheme = 'IOB2',
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suffix = False,
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)
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# change all values to regular (not numpy) floats (otherwise cannot be serialized to json)
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seqeval_results = literal_eval(str(seqeval_results))
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return(seqeval_results)
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