eladsegal commited on
Commit
f9b7595
1 Parent(s): 2cdb235

Update metrics/scrolls.py

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  1. metrics/scrolls.py +26 -6
metrics/scrolls.py CHANGED
@@ -31,24 +31,44 @@ Returns: depending on the Scrolls subset, one or several of:
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  "exact_match": Exact Match score
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  "f1": F1 score
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  "rouge": ROUGE score
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  Examples:
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  predictions = ["exact match example", "hello there", "general kenobi"] # List[str]
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  references = [["exact match example"], ["hello", "hi there"], ["commander kenobi"]] # List[List[str]]
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- >>> scrolls_metric = datasets.load_metric('src/metrics/scrolls.py', 'gov_report') # 'gov_report' or any of ["qmsum", "summ_screen_fd"]
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  >>> results = scrolls_metric.compute(predictions=predictions, references=references)
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  >>> print(results)
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- {'rouge/rouge1': 72.2222, 'rouge/rouge2': 33.3333, 'rouge/rougeL': 72.2222, 'rouge/rougeLsum': 72.2222, 'rouge/geometric_mean': 55.8136, 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667}
 
 
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- >>> scrolls_metric = datasets.load_metric('src/metrics/scrolls.py', 'contract_nli') # 'contract_nli' or any of ["quality", "quality_hard"]
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  >>> results = scrolls_metric.compute(predictions=predictions, references=references)
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  >>> print(results)
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- {'exact_match': 33.3333, 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667}
 
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- >>> scrolls_metric = datasets.load_metric('src/metrics/scrolls.py', 'narrative_qa') # 'narrative_qa' or any of ["qasper"]
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  >>> results = scrolls_metric.compute(predictions=predictions, references=references)
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  >>> print(results)
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- {'f1': 72.2222, 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667}
 
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  """
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  DATASET_TO_METRICS = {
 
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  "exact_match": Exact Match score
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  "f1": F1 score
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  "rouge": ROUGE score
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+
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+ Use the following code to download the metric:
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+ ```
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+ import os, shutil
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+ from huggingface_hub import hf_hub_download
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+ def download_metric():
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+ scrolls_metric_path = hf_hub_download(repo_id="datasets/tau/scrolls", filename="metrics/scrolls.py")
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+ updated_scrolls_metric_path = (
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+ os.path.dirname(scrolls_metric_path) + os.path.basename(scrolls_metric_path).replace(".", "_") + ".py"
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+ )
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+ shutil.copy(scrolls_metric_path, updated_scrolls_metric_path)
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+ return updated_scrolls_metric_path
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+
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+ scrolls_metric_path = download_metric()
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+ ```
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+
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  Examples:
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  predictions = ["exact match example", "hello there", "general kenobi"] # List[str]
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  references = [["exact match example"], ["hello", "hi there"], ["commander kenobi"]] # List[List[str]]
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+ >>> scrolls_metric = datasets.load_metric(scrolls_metric_path, 'gov_report') # 'gov_report' or any of ["qmsum", "summ_screen_fd"]
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  >>> results = scrolls_metric.compute(predictions=predictions, references=references)
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  >>> print(results)
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+ {'rouge/rouge1': 72.2222, 'rouge/rouge2': 33.3333, 'rouge/rougeL': 72.2222, 'rouge/rougeLsum': 72.2222, 'rouge/geometric_mean': 55.8136,
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+ 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667, 'scrolls_score': 55.8136,
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+ 'display_keys': ['rouge/rouge1', 'rouge/rouge2', 'rouge/rougeL'], 'display': [72.2222, 33.3333, 72.2222]}
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+ >>> scrolls_metric = datasets.load_metric(scrolls_metric_path, 'contract_nli') # 'contract_nli' or "quality"
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  >>> results = scrolls_metric.compute(predictions=predictions, references=references)
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  >>> print(results)
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+ {'exact_match': 33.3333, 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667, 'scrolls_score': 33.3333,
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+ 'display_keys': ['exact_match'], 'display': [33.3333]}
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+ >>> scrolls_metric = datasets.load_metric(scrolls_metric_path, 'narrative_qa') # 'narrative_qa' or "qasper"
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  >>> results = scrolls_metric.compute(predictions=predictions, references=references)
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  >>> print(results)
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+ {'f1': 72.2222, 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667, 'scrolls_score': 72.2222,
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+ 'display_keys': ['f1'], 'display': [72.2222]}
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  """
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  DATASET_TO_METRICS = {