Duplicate from evaluate-metric/squad

Co-authored-by: Julien Chaumond <julien-c@users.noreply.huggingface.co>

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README.md

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+---
+title: SQuAD
+emoji: 🤗
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 3.0.2
+app_file: app.py
+pinned: false
+tags:
+- evaluate
+- metric
+description: >-
+  This metric wrap the official scoring script for version 1 of the Stanford
+  Question Answering Dataset (SQuAD).
+
+  Stanford Question Answering Dataset (SQuAD) is a reading comprehension
+  dataset, consisting of questions posed by crowdworkers on a set of Wikipedia
+  articles, where the answer to every question is a segment of text, or span,
+  from the corresponding reading passage, or the question might be unanswerable.
+duplicated_from: evaluate-metric/squad
+---
+
+# Metric Card for SQuAD
+
+## Metric description
+This metric wraps the official scoring script for version 1 of the [Stanford Question Answering Dataset (SQuAD)](https://huggingface.co/datasets/squad). 
+
+SQuAD is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
+
+## How to use 
+
+The metric takes two files or two lists of question-answers dictionaries as inputs : one with the predictions of the model and the other with the references to be compared to:
+
+```python
+from evaluate import load
+squad_metric = load("squad")
+results = squad_metric.compute(predictions=predictions, references=references)
+```
+## Output values
+
+This metric outputs a dictionary with two values: the average exact match score and the average [F1 score](https://huggingface.co/metrics/f1).
+
+```
+{'exact_match': 100.0, 'f1': 100.0}
+```
+
+The range of `exact_match` is 0-100, where 0.0 means no answers were matched and 100.0 means all answers were matched. 
+
+The range of `f1` is 0-1 -- its lowest possible value is 0, if either the precision or the recall is 0, and its highest possible value is 1.0, which means perfect precision and recall.
+
+### Values from popular papers
+The [original SQuAD paper](https://nlp.stanford.edu/pubs/rajpurkar2016squad.pdf) reported an F1 score of 51.0% and an Exact Match score of 40.0%. They also report that human performance on the dataset represents an F1 score of 90.5% and an Exact Match score of 80.3%.
+
+For more recent model performance, see the [dataset leaderboard](https://paperswithcode.com/dataset/squad).
+
+## Examples 
+
+Maximal values for both exact match and F1 (perfect match):
+
+```python
+from evaluate import load
+squad_metric = load("squad")
+predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
+references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
+results = squad_metric.compute(predictions=predictions, references=references)
+results
+{'exact_match': 100.0, 'f1': 100.0}
+```
+
+Minimal values for both exact match and F1 (no match):
+
+```python
+from evaluate import load
+squad_metric = load("squad")
+predictions = [{'prediction_text': '1999', 'id': '56e10a3be3433e1400422b22'}]
+references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
+results = squad_metric.compute(predictions=predictions, references=references)
+results
+{'exact_match': 0.0, 'f1': 0.0}
+```
+
+Partial match (2 out of 3 answers correct) : 
+
+```python
+from evaluate import load
+squad_metric = load("squad")
+predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}, {'prediction_text': 'Beyonce', 'id': '56d2051ce7d4791d0090260b'},  {'prediction_text': 'climate change', 'id': '5733b5344776f419006610e1'}]
+references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}, {'answers': {'answer_start': [233], 'text': ['Beyoncé and Bruno Mars']}, 'id': '56d2051ce7d4791d0090260b'}, {'answers': {'answer_start': [891], 'text': ['climate change']}, 'id': '5733b5344776f419006610e1'}]
+results = squad_metric.compute(predictions=predictions, references=references)
+results
+{'exact_match': 66.66666666666667, 'f1': 66.66666666666667}
+```
+
+## Limitations and bias
+This metric works only with datasets that have the same format as [SQuAD v.1 dataset](https://huggingface.co/datasets/squad).
+
+The SQuAD dataset does contain a certain amount of noise, such as duplicate questions as well as missing answers, but these represent a minority of the 100,000 question-answer pairs. Also, neither exact match nor F1 score reflect whether models do better on certain types of questions (e.g. who questions) or those that cover a certain gender or geographical area -- carrying out more in-depth error analysis can complement these numbers. 
+
+
+## Citation
+
+    @inproceedings{Rajpurkar2016SQuAD10,
+    title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
+    author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
+    booktitle={EMNLP},
+    year={2016}
+    }
+    
+## Further References 
+
+- [The Stanford Question Answering Dataset: Background, Challenges, Progress (blog post)](https://rajpurkar.github.io/mlx/qa-and-squad/)
+- [Hugging Face Course -- Question Answering](https://huggingface.co/course/chapter7/7)

app.py

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+import evaluate
+from evaluate.utils import launch_gradio_widget
+
+
+module = evaluate.load("squad")
+launch_gradio_widget(module)

compute_score.py

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+""" Official evaluation script for v1.1 of the SQuAD dataset. """
+
+import argparse
+import json
+import re
+import string
+import sys
+from collections import Counter
+
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+
+    def remove_articles(text):
+        return re.sub(r"\b(a|an|the)\b", " ", text)
+
+    def white_space_fix(text):
+        return " ".join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return "".join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def f1_score(prediction, ground_truth):
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def exact_match_score(prediction, ground_truth):
+    return normalize_answer(prediction) == normalize_answer(ground_truth)
+
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+
+def compute_score(dataset, predictions):
+    f1 = exact_match = total = 0
+    for article in dataset:
+        for paragraph in article["paragraphs"]:
+            for qa in paragraph["qas"]:
+                total += 1
+                if qa["id"] not in predictions:
+                    message = "Unanswered question " + qa["id"] + " will receive score 0."
+                    print(message, file=sys.stderr)
+                    continue
+                ground_truths = list(map(lambda x: x["text"], qa["answers"]))
+                prediction = predictions[qa["id"]]
+                exact_match += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths)
+                f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths)
+
+    exact_match = 100.0 * exact_match / total
+    f1 = 100.0 * f1 / total
+
+    return {"exact_match": exact_match, "f1": f1}
+
+
+if __name__ == "__main__":
+    expected_version = "1.1"
+    parser = argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version)
+    parser.add_argument("dataset_file", help="Dataset file")
+    parser.add_argument("prediction_file", help="Prediction File")
+    args = parser.parse_args()
+    with open(args.dataset_file) as dataset_file:
+        dataset_json = json.load(dataset_file)
+        if dataset_json["version"] != expected_version:
+            print(
+                "Evaluation expects v-" + expected_version + ", but got dataset with v-" + dataset_json["version"],
+                file=sys.stderr,
+            )
+        dataset = dataset_json["data"]
+    with open(args.prediction_file) as prediction_file:
+        predictions = json.load(prediction_file)
+    print(json.dumps(compute_score(dataset, predictions)))

requirements.txt

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+git+https://github.com/huggingface/evaluate@6abb0d53b82b1e5efea5d683b91d7990a653c78d

squad.py

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+# Copyright 2020 The HuggingFace Evaluate Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" SQuAD metric. """
+
+import datasets
+
+import evaluate
+
+from .compute_score import compute_score
+
+
+_CITATION = """\
+@inproceedings{Rajpurkar2016SQuAD10,
+  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
+  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
+  booktitle={EMNLP},
+  year={2016}
+}
+"""
+
+_DESCRIPTION = """
+This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).
+
+Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
+crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
+from the corresponding reading passage, or the question might be unanswerable.
+"""
+
+_KWARGS_DESCRIPTION = """
+Computes SQuAD scores (F1 and EM).
+Args:
+    predictions: List of question-answers dictionaries with the following key-values:
+        - 'id': id of the question-answer pair as given in the references (see below)
+        - 'prediction_text': the text of the answer
+    references: List of question-answers dictionaries with the following key-values:
+        - 'id': id of the question-answer pair (see above),
+        - 'answers': a Dict in the SQuAD dataset format
+            {
+                'text': list of possible texts for the answer, as a list of strings
+                'answer_start': list of start positions for the answer, as a list of ints
+            }
+            Note that answer_start values are not taken into account to compute the metric.
+Returns:
+    'exact_match': Exact match (the normalized answer exactly match the gold answer)
+    'f1': The F-score of predicted tokens versus the gold answer
+Examples:
+
+    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
+    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
+    >>> squad_metric = evaluate.load("squad")
+    >>> results = squad_metric.compute(predictions=predictions, references=references)
+    >>> print(results)
+    {'exact_match': 100.0, 'f1': 100.0}
+"""
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class Squad(evaluate.Metric):
+    def _info(self):
+        return evaluate.MetricInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "predictions": {"id": datasets.Value("string"), "prediction_text": datasets.Value("string")},
+                    "references": {
+                        "id": datasets.Value("string"),
+                        "answers": datasets.features.Sequence(
+                            {
+                                "text": datasets.Value("string"),
+                                "answer_start": datasets.Value("int32"),
+                            }
+                        ),
+                    },
+                }
+            ),
+            codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],
+            reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],
+        )
+
+    def _compute(self, predictions, references):
+        pred_dict = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
+        dataset = [
+            {
+                "paragraphs": [
+                    {
+                        "qas": [
+                            {
+                                "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
+                                "id": ref["id"],
+                            }
+                            for ref in references
+                        ]
+                    }
+                ]
+            }
+        ]
+        score = compute_score(dataset=dataset, predictions=pred_dict)
+        return score