README.md

---
dataset_info:
- config_name: default
  features:
  - name: utterance
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 40588
    num_examples: 431
  - name: test
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    num_examples: 108
  download_size: 25385
  dataset_size: 50619
- config_name: intents
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    dtype: int64
  - name: name
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  - name: tags
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  - name: regexp_full_match
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  - name: regexp_partial_match
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  - name: description
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  splits:
  - name: intents
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    num_examples: 14
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  dataset_size: 280
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
---

# minds14

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

minds14 = Dataset.from_datasets("AutoIntent/minds14")
```

## Source

This dataset is taken from `PolyAI/minds14` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):

```python
from datasets import load_dataset
from autointent import Dataset

def convert_minds14(minds14_train):
    utterances = []

    for batch in minds14_train.iter(batch_size=16, drop_last_batch=False):
        for txt, intent_id in zip(batch["english_transcription"], batch["intent_class"], strict=False):
            utterances.append({"utterance": txt, "label": intent_id})

    return Dataset.from_dict({"train": utterances})

minds14 = load_dataset("PolyAI/minds14", "ru-RU", trust_remote_code=True)
minds14_converted = convert_minds14(minds14["train"])
```