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HalteroXHunter
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80d7919
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Parent(s):
d5750c7
mean for bertscore and bleurt
Browse files- generation_evaluator.py +17 -6
- requirements.txt +2 -1
generation_evaluator.py
CHANGED
@@ -1,5 +1,6 @@
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import datasets
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import evaluate
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_CITATION = """\
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@inproceedings{lin-2004-rouge,
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@@ -109,7 +110,7 @@ BLEU:{
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EXACT_MATCH:{
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"exact_match": exact_match rate. Possible values are between 0.0 and 1.0, inclusive.
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},
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BERT_SCORE:{
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"precision": Precision.
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"recall": Recall.
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"f1": F1 score.
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@@ -158,22 +159,32 @@ class GenerationEvaluator(evaluate.Metric):
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exact_match_results = exact_match_score.compute(
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predictions=predictions, references=references
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)
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bert_score = evaluate.load("bertscore")
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bert_score_results = bert_score.compute(
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predictions=predictions, references=references,
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lang="en"
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)
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bleurt_score = evaluate.load("bleurt", module_type="metric")
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bleurt_results = bleurt_score.compute(
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predictions=predictions, references=references
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)
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return {
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"ROUGE": rouge_results,
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"BLEU": bleu_results,
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"EXACT_MATCH": exact_match_results,
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"BERT_SCORE":bert_score_results,
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"BLEURT":bleurt_results
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}
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import datasets
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import evaluate
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import numpy as np
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_CITATION = """\
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@inproceedings{lin-2004-rouge,
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EXACT_MATCH:{
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"exact_match": exact_match rate. Possible values are between 0.0 and 1.0, inclusive.
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},
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BERT_SCORE:{
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"precision": Precision.
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"recall": Recall.
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"f1": F1 score.
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exact_match_results = exact_match_score.compute(
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predictions=predictions, references=references
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)
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bert_score = evaluate.load("bertscore")
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bert_score_results = bert_score.compute(
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predictions=predictions, references=references, lang="en"
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)
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mean_precision = np.mean(bert_score_results['precision'])
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mean_recall = np.mean(bert_score_results['recall'])
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mean_f1 = np.mean(bert_score_results['f1'])
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bert_score_results['precision'] = round(mean_precision, 4)
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bert_score_results['recall'] = round(mean_recall, 4)
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bert_score_results['f1'] = round(mean_f1, 4)
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bleurt_score = evaluate.load("bleurt", module_type="metric")
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bleurt_results = bleurt_score.compute(
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predictions=predictions, references=references
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)
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mean_bleurt_score = np.mean(bleurt_results['scores'])
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bleurt_results['scores'] = round(mean_bleurt_score, 4)
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return {
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"ROUGE": rouge_results,
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"BLEU": bleu_results,
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"EXACT_MATCH": exact_match_results,
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"BERT_SCORE": bert_score_results,
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"BLEURT": bleurt_results,
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}
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requirements.txt
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@@ -3,4 +3,5 @@ datasets
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scikit-learn
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gradio
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bert_score
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git+https://github.com/google-research/bleurt.git
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scikit-learn
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gradio
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bert_score
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git+https://github.com/google-research/bleurt.git
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numpy
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