Elad commited on
Commit
fcc2240
1 Parent(s): ed00236

add metrics

Browse files
metrics/bart_score.py ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ from BARTScore.bart_score import BARTScorer
3
+
4
+
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+ def get_scorers():
6
+ assert os.path.isfile(
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+ os.path.join("BARTScore", "bart.pth")
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+ ), "You must download `bart.pth` to use BARTScore.\nUse `gdown --id 1_7JfF7KOInb7ZrxKHIigTMR4ChVET01m --output bart.pth`"
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+
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+ scorers = {}
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+
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+ scorers["vanilla"] = BARTScorer(device="cuda:0", checkpoint="facebook/bart-large")
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+
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+ scorers["cnn"] = BARTScorer(device="cuda:0", checkpoint="facebook/bart-large-cnn")
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+
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+ # for the parabank model, first init a bart model, then load the local para model from BARTScore/bart.pth
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+ # see the documentation from https://github.com/neulab/BARTScore for reference
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+ scorers["para"] = BARTScorer(device="cuda:0", checkpoint="facebook/bart-large-cnn")
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+ scorers["para"].load(path="BARTScore/bart.pth")
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+
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+ return scorers
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+
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+
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+ def compute_bart_score_for_scorer(predictions, references, scorer_name, scorer):
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+ precisions = scorer.score(predictions, references, batch_size=4)
26
+ recalls = scorer.score(references, predictions, batch_size=4)
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+ f_scores = 0.5 * (precisions + recalls)
28
+
29
+ return [
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+ {
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+ f"{scorer_name}_f_score": f_scores[i],
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+ f"{scorer_name}_precision": precisions[i],
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+ f"{scorer_name}_recall": recalls[i],
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+ }
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+ for i in len(range(predictions))
36
+ ]
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+
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+
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+ def compute_bart_score(predictions, references, scorers):
40
+ result = [{} for _ in len(range(predictions))]
41
+ for scorer_name, scorer in scorers.items():
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+ scorer_result = compute_bart_score_for_scorer(predictions, references, scorer_name, scorer)
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+ for i, element in enumerate(scorer_result):
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+ result[i].update(element)
45
+ return result
metrics/bleu.py CHANGED
@@ -107,4 +107,16 @@ def compute_bleu(reference_corpus, translation_corpus, max_order=4, smooth=False
107
 
108
  bleu = geo_mean * bp
109
 
110
- return (bleu, precisions, bp, ratio, translation_length, reference_length)
 
 
 
 
 
 
 
 
 
 
 
 
 
107
 
108
  bleu = geo_mean * bp
109
 
110
+ return {
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+ "bleu": bleu,
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+ **{f"precision-{i+1}": round(p, 4) for i, p in enumerate(precisions)},
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+ "brevity_penalty": bp,
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+ "length_ratio": ratio,
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+ "translation_length": translation_length,
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+ "reference_length": reference_length,
117
+ }
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+
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+
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+ def bleu_postprocess_text(text):
121
+ # TODO: Tokenize properly
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+ return text.split()
metrics/exact_match.py ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import re
2
+ import string
3
+
4
+
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+ def normalize_answer(s):
6
+ """Lower text and remove punctuation, articles and extra whitespace."""
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+
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+ def remove_articles(text):
9
+ return re.sub(r"\b(a|an|the)\b", " ", text)
10
+
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+ def white_space_fix(text):
12
+ return " ".join(text.split())
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+
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+ def remove_punc(text):
15
+ exclude = set(string.punctuation)
16
+ return "".join(ch for ch in text if ch not in exclude)
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+
18
+ def lower(text):
19
+ return text.lower()
20
+
21
+ return white_space_fix(remove_articles(remove_punc(lower(s))))
22
+
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+
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+ def exact_match_score(prediction, ground_truth):
25
+ return normalize_answer(prediction) == normalize_answer(ground_truth)
26
+
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+
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+ def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
29
+ scores_for_ground_truths = []
30
+ for ground_truth in ground_truths:
31
+ score = metric_fn(prediction, ground_truth)
32
+ scores_for_ground_truths.append(score)
33
+ return max(scores_for_ground_truths)
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+
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+
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+ def compute_exact_match(predictions, references):
37
+ exact_match = 0
38
+ for prediction, ground_truths in zip(predictions, references):
39
+ exact_match += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths)
40
+ return 100.0 * exact_match / len(predictions)
metrics/f1.py ADDED
@@ -0,0 +1,56 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copied from https://github.com/huggingface/datasets/blob/d3c7b9481d427ce41256edaf6773c47570f06f3b/metrics/squad/evaluate.py
2
+
3
+ import re
4
+ import string
5
+ from collections import Counter
6
+
7
+
8
+ def normalize_answer(s):
9
+ """Lower text and remove punctuation, articles and extra whitespace."""
10
+
11
+ def remove_articles(text):
12
+ return re.sub(r"\b(a|an|the)\b", " ", text)
13
+
14
+ def white_space_fix(text):
15
+ return " ".join(text.split())
16
+
17
+ def remove_punc(text):
18
+ exclude = set(string.punctuation)
19
+ return "".join(ch for ch in text if ch not in exclude)
20
+
21
+ def lower(text):
22
+ return text.lower()
23
+
24
+ return white_space_fix(remove_articles(remove_punc(lower(s))))
25
+
26
+
27
+ def f1_score(prediction, ground_truth):
28
+ prediction_tokens = normalize_answer(prediction).split()
29
+ ground_truth_tokens = normalize_answer(ground_truth).split()
30
+ common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
31
+ num_same = sum(common.values())
32
+ if num_same == 0:
33
+ return 0
34
+ precision = 1.0 * num_same / len(prediction_tokens)
35
+ recall = 1.0 * num_same / len(ground_truth_tokens)
36
+ f1 = (2 * precision * recall) / (precision + recall)
37
+ return f1
38
+
39
+
40
+ def exact_match_score(prediction, ground_truth):
41
+ return normalize_answer(prediction) == normalize_answer(ground_truth)
42
+
43
+
44
+ def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
45
+ scores_for_ground_truths = []
46
+ for ground_truth in ground_truths:
47
+ score = metric_fn(prediction, ground_truth)
48
+ scores_for_ground_truths.append(score)
49
+ return max(scores_for_ground_truths)
50
+
51
+
52
+ def compute_f1(predictions, references):
53
+ f1 = 0
54
+ for prediction, ground_truths in zip(predictions, references):
55
+ f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths)
56
+ return 100.0 * f1 / len(predictions)
metrics/rouge.py CHANGED
@@ -4,24 +4,24 @@ import nltk
4
  from rouge_score import rouge_scorer, scoring
5
 
6
 
7
- def compute_rouge(predictions, references, rouge_types=None, use_agregator=True, use_stemmer=False):
8
  if rouge_types is None:
9
  rouge_types = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
10
 
11
  scorer = rouge_scorer.RougeScorer(rouge_types=rouge_types, use_stemmer=use_stemmer)
12
- if use_agregator:
13
  aggregator = scoring.BootstrapAggregator()
14
  else:
15
  scores = []
16
 
17
  for ref, pred in zip(references, predictions):
18
  score = scorer.score(ref, pred)
19
- if use_agregator:
20
  aggregator.add_scores(score)
21
  else:
22
  scores.append(score)
23
 
24
- if use_agregator:
25
  result = aggregator.aggregate()
26
  else:
27
  result = {}
@@ -33,7 +33,6 @@ def compute_rouge(predictions, references, rouge_types=None, use_agregator=True,
33
 
34
  # TODO: Check if it is necessary
35
  # Copied from https://github.com/huggingface/transformers/blob/3977b58437b8ce1ea1da6e31747d888efec2419b/examples/pytorch/summarization/run_summarization.py#L520
36
- def rouge_postprocess_text(texts):
37
  # rougeLSum expects newline after each sentence
38
- texts = ["\n".join(nltk.sent_tokenize(text)) for text in texts]
39
- return texts
 
4
  from rouge_score import rouge_scorer, scoring
5
 
6
 
7
+ def compute_rouge(predictions, references, rouge_types=None, use_aggregator=True, use_stemmer=False):
8
  if rouge_types is None:
9
  rouge_types = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
10
 
11
  scorer = rouge_scorer.RougeScorer(rouge_types=rouge_types, use_stemmer=use_stemmer)
12
+ if use_aggregator:
13
  aggregator = scoring.BootstrapAggregator()
14
  else:
15
  scores = []
16
 
17
  for ref, pred in zip(references, predictions):
18
  score = scorer.score(ref, pred)
19
+ if use_aggregator:
20
  aggregator.add_scores(score)
21
  else:
22
  scores.append(score)
23
 
24
+ if use_aggregator:
25
  result = aggregator.aggregate()
26
  else:
27
  result = {}
 
33
 
34
  # TODO: Check if it is necessary
35
  # Copied from https://github.com/huggingface/transformers/blob/3977b58437b8ce1ea1da6e31747d888efec2419b/examples/pytorch/summarization/run_summarization.py#L520
36
+ def rouge_postprocess_text(text):
37
  # rougeLSum expects newline after each sentence
38
+ return "\n".join(nltk.sent_tokenize(text))