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