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import glob |
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import os |
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import random |
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from tqdm import tqdm |
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from datasets import Dataset, DatasetDict, load_dataset |
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def convert(list_of_dicts): |
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res = {} |
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for d in list_of_dicts: |
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for k, v in d.items(): |
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res.setdefault(k, []).append(v) |
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return res |
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v10 = load_dataset("fever", "v1.0") |
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name_lst = ["train", "labelled_dev"] |
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old_to_new_label_map = {"SUPPORTS": "supported", "REFUTES": "refuted"} |
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data_map = {} |
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for name in name_lst: |
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instance_lst = [] |
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for entry in tqdm(v10[name]): |
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id_ = entry["id"] |
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label = entry["label"] |
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claim = entry["claim"] |
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evidence_id = entry["evidence_id"] |
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evidence_wiki_url = entry["evidence_wiki_url"] |
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if evidence_id != -1: |
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assert label in {"SUPPORTS", "REFUTES"} |
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instance = {"id": id_, "label": old_to_new_label_map[label], "claim": claim} |
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instance_lst.append(instance) |
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key = "dev" if name in {"labelled_dev"} else name |
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instance_lst = sorted([dict(t) for t in {tuple(d.items()) for d in instance_lst}], key=lambda d: d["claim"]) |
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label_to_instance_lst = {} |
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for e in instance_lst: |
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if e["label"] not in label_to_instance_lst: |
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label_to_instance_lst[e["label"]] = [] |
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label_to_instance_lst[e["label"]].append(e) |
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min_len = min(len(v) for k, v in label_to_instance_lst.items()) |
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new_instance_lst = [] |
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for k in sorted(label_to_instance_lst.keys()): |
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new_instance_lst += label_to_instance_lst[k][:min_len] |
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random.Random(42).shuffle(new_instance_lst) |
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data_map[key] = new_instance_lst |
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ds_path = "pminervini/hl-fever" |
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task_to_ds_map = {k: Dataset.from_dict(convert(v)) for k, v in data_map.items()} |
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ds_dict = DatasetDict(task_to_ds_map) |
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ds_dict.push_to_hub(ds_path, "v1.0") |
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