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Update Space (evaluate main: c447fc8e)
Browse files- requirements.txt +1 -1
- squad_v2.py +4 -23
requirements.txt
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git+https://github.com/huggingface/evaluate@
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git+https://github.com/huggingface/evaluate@c447fc8eda9c62af501bfdc6988919571050d950
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squad_v2.py
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# limitations under the License.
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""" SQuAD v2 metric. """
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from dataclasses import dataclass
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import datasets
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import evaluate
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"""
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@dataclass
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class SquadV2Config(evaluate.info.Config):
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name: str = "default"
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no_answer_threshold: float = 1.0
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@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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class SquadV2(evaluate.Metric):
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CONFIG_CLASS = SquadV2Config
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ALLOWED_CONFIG_NAMES = ["default"]
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def _info(self, config):
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return evaluate.MetricInfo(
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description=_DESCRIPTION,
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citation=_CITATION,
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inputs_description=_KWARGS_DESCRIPTION,
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config=config,
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features=datasets.Features(
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{
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"predictions": {
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reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],
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)
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def _compute(self, predictions, references):
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no_answer_probabilities = {p["id"]: p["no_answer_probability"] for p in predictions}
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dataset = [{"paragraphs": [{"qas": references}]}]
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predictions = {p["id"]: p["prediction_text"] for p in predictions}
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no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
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exact_raw, f1_raw = get_raw_scores(dataset, predictions)
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exact_thresh = apply_no_ans_threshold(
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)
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f1_thresh = apply_no_ans_threshold(
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f1_raw, no_answer_probabilities, qid_to_has_ans, self.config.no_answer_threshold
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)
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out_eval = make_eval_dict(exact_thresh, f1_thresh)
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if has_ans_qids:
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# limitations under the License.
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""" SQuAD v2 metric. """
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import datasets
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import evaluate
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"""
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@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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class SquadV2(evaluate.Metric):
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def _info(self):
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return evaluate.MetricInfo(
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description=_DESCRIPTION,
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citation=_CITATION,
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inputs_description=_KWARGS_DESCRIPTION,
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features=datasets.Features(
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{
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"predictions": {
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reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],
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)
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def _compute(self, predictions, references, no_answer_threshold=1.0):
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no_answer_probabilities = {p["id"]: p["no_answer_probability"] for p in predictions}
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dataset = [{"paragraphs": [{"qas": references}]}]
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predictions = {p["id"]: p["prediction_text"] for p in predictions}
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no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
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exact_raw, f1_raw = get_raw_scores(dataset, predictions)
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exact_thresh = apply_no_ans_threshold(exact_raw, no_answer_probabilities, qid_to_has_ans, no_answer_threshold)
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f1_thresh = apply_no_ans_threshold(f1_raw, no_answer_probabilities, qid_to_has_ans, no_answer_threshold)
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out_eval = make_eval_dict(exact_thresh, f1_thresh)
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if has_ans_qids:
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