---
dataset_info:
- config_name: default
  features:
  - name: utterance
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 406785
    num_examples: 8954
  - name: test
    num_bytes: 49545
    num_examples: 1076
  download_size: 199496
  dataset_size: 456330
- 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: 2422
    num_examples: 64
  download_size: 4037
  dataset_size: 2422
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
---

# hwu64

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

hwu64 = Dataset.from_hub("AutoIntent/hwu64")
```

## Source

This dataset is taken from original work's github repository `jianguoz/Few-Shot-Intent-Detection` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):

```python
# define utils
import requests
from autointent import Dataset

def load_text_from_url(github_file: str):
    return requests.get(github_file).text

def convert_hwu64(hwu_utterances, hwu_labels):
    intent_names = sorted(set(hwu_labels))
    name_to_id = dict(zip(intent_names, range(len(intent_names)), strict=False))
    n_classes = len(intent_names)

    assert len(hwu_utterances) == len(hwu_labels)

    classwise_utterance_records = [[] for _ in range(n_classes)]
    intents = [
        {
            "id": i,
            "name": name,
            
        }
        for i, name in enumerate(intent_names)
    ]

    for txt, name in zip(hwu_utterances, hwu_labels, strict=False):
        intent_id = name_to_id[name]
        target_list = classwise_utterance_records[intent_id]
        target_list.append({"utterance": txt, "label": intent_id})

    utterances = [rec for lst in classwise_utterance_records for rec in lst]
    return {"intents": intents, split: utterances}

# load
file_url = "https://raw.githubusercontent.com/jianguoz/Few-Shot-Intent-Detection/refs/heads/main/Datasets/HWU64/train/label"
labels = load_text_from_url(file_url).split("\n")[:-1]
file_url = "https://raw.githubusercontent.com/jianguoz/Few-Shot-Intent-Detection/refs/heads/main/Datasets/HWU64/train/seq.in"
utterances = load_text_from_url(file_url).split("\n")[:-1]
# convert
hwu64_train = convert_hwu64(utterances, labels, "train")

file_url = "https://raw.githubusercontent.com/jianguoz/Few-Shot-Intent-Detection/refs/heads/main/Datasets/HWU64/test/label"
labels = load_text_from_url(file_url).split("\n")[:-1]
file_url = "https://raw.githubusercontent.com/jianguoz/Few-Shot-Intent-Detection/refs/heads/main/Datasets/HWU64/test/seq.in"
utterances = load_text_from_url(file_url).split("\n")[:-1]
# convert
hwu64_test = convert_hwu64(utterances, labels, "test")

hwu64_train["test"] = hwu64_test["test"]
dataset = Dataset.from_dict(hwu64_train)

```