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--- |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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dataset_info: |
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features: |
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- name: tokens |
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sequence: string |
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- name: ner_tags |
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sequence: |
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class_label: |
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names: |
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'0': O |
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'1': B-ORG |
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'2': I-ORG |
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splits: |
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- name: train |
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num_bytes: 40381520.59961503 |
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num_examples: 109424 |
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- name: validation |
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num_bytes: 5782294.96333573 |
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num_examples: 15908 |
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- name: test |
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num_bytes: 10727120.198367199 |
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num_examples: 28124 |
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download_size: 14938552 |
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dataset_size: 56890935.76131796 |
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--- |
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# Dataset Card for "ner-orgs" |
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This dataset is a concatenation of subsets of [Few-NERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd), [CoNLL 2003](https://huggingface.co/datasets/conll2003) and [OntoNotes v5](https://huggingface.co/datasets/tner/ontonotes5), but only the "B-ORG" and "I-ORG" labels. |
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Exactly half of the samples per split contain organisations, while the other half do not contain any. |
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It was generated using the following script: |
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```py |
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import random |
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from datasets import load_dataset, concatenate_datasets, Features, Sequence, ClassLabel, Value, DatasetDict |
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FEATURES = Features( |
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{ |
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"tokens": Sequence(feature=Value(dtype="string")), |
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"ner_tags": Sequence(feature=ClassLabel(names=["O", "B-ORG", "I-ORG"])), |
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} |
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) |
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def load_fewnerd(): |
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def mapper(sample): |
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sample["ner_tags"] = [int(tag == 5) for tag in sample["ner_tags"]] |
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sample["ner_tags"] = [ |
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2 if tag == 1 and idx > 0 and sample["ner_tags"][idx - 1] == 1 else tag |
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for idx, tag in enumerate(sample["ner_tags"]) |
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] |
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return sample |
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dataset = load_dataset("DFKI-SLT/few-nerd", "supervised") |
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dataset = dataset.map(mapper, remove_columns=["id", "fine_ner_tags"]) |
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dataset = dataset.cast(FEATURES) |
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return dataset |
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def load_conll(): |
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label_mapping = {3: 1, 4: 2} |
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def mapper(sample): |
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sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]] |
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return sample |
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dataset = load_dataset("conll2003") |
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dataset = dataset.map(mapper, remove_columns=["id", "pos_tags", "chunk_tags"]) |
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dataset = dataset.cast(FEATURES) |
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return dataset |
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def load_ontonotes(): |
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label_mapping = {11: 1, 12: 2} |
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def mapper(sample): |
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sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]] |
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return sample |
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dataset = load_dataset("tner/ontonotes5") |
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dataset = dataset.rename_column("tags", "ner_tags") |
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dataset = dataset.map(mapper) |
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dataset = dataset.cast(FEATURES) |
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return dataset |
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def has_org(sample): |
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return bool(sum(sample["ner_tags"])) |
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def has_no_org(sample): |
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return not has_org(sample) |
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def preprocess_raw_dataset(raw_dataset): |
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# Set the number of sentences without an org equal to the number of sentences with an org |
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dataset_org = raw_dataset.filter(has_org) |
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dataset_no_org = raw_dataset.filter(has_no_org) |
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dataset_no_org = dataset_no_org.select(random.sample(range(len(dataset_no_org)), k=len(dataset_org))) |
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dataset = concatenate_datasets([dataset_org, dataset_no_org]) |
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return dataset |
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def main() -> None: |
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fewnerd_dataset = load_fewnerd() |
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conll_dataset = load_conll() |
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ontonotes_dataset = load_ontonotes() |
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raw_train_dataset = concatenate_datasets([fewnerd_dataset["train"], conll_dataset["train"], ontonotes_dataset["train"]]) |
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raw_eval_dataset = concatenate_datasets([fewnerd_dataset["validation"], conll_dataset["validation"], ontonotes_dataset["validation"]]) |
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raw_test_dataset = concatenate_datasets([fewnerd_dataset["test"], conll_dataset["test"], ontonotes_dataset["test"]]) |
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train_dataset = preprocess_raw_dataset(raw_train_dataset) |
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eval_dataset = preprocess_raw_dataset(raw_eval_dataset) |
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test_dataset = preprocess_raw_dataset(raw_test_dataset) |
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dataset_dict = DatasetDict( |
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{ |
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"train": train_dataset, |
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"validation": eval_dataset, |
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"test": test_dataset, |
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} |
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) |
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dataset_dict.push_to_hub("ner-orgs", private=True) |
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if __name__ == "__main__": |
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main() |
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``` |
