---
dataset_info:
- config_name: default
  features:
  - name: utterance
    dtype: string
  - name: label
    sequence: int64
  splits:
  - name: train
    num_bytes: 26416704
    num_examples: 20856
  download_size: 18117453
  dataset_size: 34001126.59570387
- config_name: intents
  features:
  - name: id
    dtype: int64
  - name: name
    dtype: string
  - name: tags
    sequence: 'null'
  - name: regexp_full_match
    sequence: 'null'
  - name: regexp_partial_match
    sequence: 'null'
  - name: description
    dtype: 'null'
  splits:
  - name: intents
    num_bytes: 1924
    num_examples: 65
  download_size: 3851
  dataset_size: 1924
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
- 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_datasets("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 collections import defaultdict
from datasets import load_dataset
from autointent import Dataset

# load original data
reuters = load_dataset("ucirvine/reuters21578", "ModHayes", trust_remote_code=True)

# remove low-resource classes
counter = defaultdict(int)
for batch in reuters["train"].iter(batch_size=16):
    for labels in batch["topics"]:
        for lab in labels:
            counter[lab] += 1
names_to_remove = [name for name, cnt in counter.items() if cnt < 10]

intent_names = sorted(set(name for intents in reuters["train"]["topics"] for name in intents))
for n in names_to_remove:
    intent_names.remove(n)
name_to_id = {name: i for i, name in enumerate(intent_names)}

# extract only texts and labels
def transform(example: dict):
    return {
        "utterance": example["text"],
        "label": [name_to_id[intent_name] for intent_name in example["topics"] if intent_name not in names_to_remove],
    }
multilabel_reuters = reuters["train"].map(transform, remove_columns=reuters["train"].features.keys())

# if any out-of-scope samples
res = multilabel_reuters.to_list()
for sample in res:
    if len(sample["label"]) == 0:
        sample.pop("label")

# format
intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
reuters_converted = Dataset.from_dict({"intents": intents, "train": res})
```