|
--- |
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dataset_info: |
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- config_name: default |
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features: |
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- name: utterance |
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dtype: string |
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- name: label |
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sequence: int64 |
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splits: |
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- name: train |
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num_bytes: 7169122 |
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num_examples: 9042 |
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- name: test |
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num_bytes: 450937 |
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num_examples: 358 |
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download_size: 8973442 |
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dataset_size: 7620059 |
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- config_name: intents |
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features: |
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- name: id |
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dtype: int64 |
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- name: name |
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dtype: string |
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- name: tags |
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sequence: 'null' |
|
- name: regex_full_match |
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sequence: 'null' |
|
- name: regex_partial_match |
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sequence: 'null' |
|
- name: description |
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dtype: 'null' |
|
splits: |
|
- name: intents |
|
num_bytes: 291 |
|
num_examples: 10 |
|
download_size: 3034 |
|
dataset_size: 291 |
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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: test |
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path: data/test-* |
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- config_name: intents |
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data_files: |
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- split: intents |
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path: intents/intents-* |
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task_categories: |
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- text-classification |
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language: |
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- en |
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--- |
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# reuters |
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This is a text classification dataset. It is intended for machine learning research and experimentation. |
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This dataset is obtained via formatting another publicly available data to be compatible with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html). |
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## Usage |
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It is intended to be used with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html): |
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```python |
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from autointent import Dataset |
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reuters = Dataset.from_hub("AutoIntent/reuters") |
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``` |
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## Source |
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This dataset is taken from `ucirvine/reuters21578` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html): |
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```python |
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from autointent import Dataset |
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import datasets |
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def get_intents_info(ds: datasets.DatasetDict) -> list[str]: |
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return sorted(set(name for intents in ds["train"]["topics"] for name in intents)) |
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def parse(ds: datasets.Dataset, intent_names: list[str]) -> list[dict]: |
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return [{ |
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"utterance": example["text"], |
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"label": [int(name in example["topics"]) for name in intent_names] |
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} for example in ds] |
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def get_low_resource_classes_mask(ds: list[dict], intent_names: list[str], fraction_thresh: float = 0.01) -> list[bool]: |
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res = [0] * len(intent_names) |
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for sample in ds: |
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for i, indicator in enumerate(sample["label"]): |
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res[i] += indicator |
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for i in range(len(intent_names)): |
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res[i] /= len(ds) |
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return [(frac < fraction_thresh) for frac in res] |
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def remove_low_resource_classes(ds: datasets.Dataset, mask: list[bool]) -> list[dict]: |
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res = [] |
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for sample in ds: |
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if sum(sample["label"]) == 1 and mask[sample["label"].index(1)]: |
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continue |
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sample["label"] = [ |
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indicator for indicator, low_resource in |
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zip(sample["label"], mask, strict=True) if not low_resource |
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] |
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res.append(sample) |
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return res |
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def remove_oos(ds: list[dict]): |
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return [sample for sample in ds if sum(sample["label"]) != 0] |
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if __name__ == "__main__": |
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reuters = datasets.load_dataset("ucirvine/reuters21578", "ModHayes", trust_remote_code=True) |
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intent_names = get_intents_info(reuters) |
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train_parsed = parse(reuters["train"], intent_names) |
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test_parsed = parse(reuters["test"], intent_names) |
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mask = get_low_resource_classes_mask(train_parsed, intent_names) |
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intent_names = [name for i, name in enumerate(intent_names) if not mask[i]] |
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train_filtered = remove_oos(remove_low_resource_classes(train_parsed, mask)) |
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test_filtered = remove_oos(remove_low_resource_classes(test_parsed, mask)) |
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intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)] |
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reuters_converted = Dataset.from_dict({"intents": intents, "train": train_filtered, "test": test_filtered}) |
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``` |
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