README.md

---
task_categories:
- text-classification

---
# AutoTrain Dataset for project: massive-4-catalan

## Dataset Description

This dataset has been automatically processed by AutoTrain for project massive-4-catalan.

### Languages

The BCP-47 code for the dataset's language is unk.

## Dataset Structure

### Data Instances

A sample from this dataset looks as follows:

```json
[
  {
    "feat_id": "1",
    "feat_locale": "ca-ES",
    "feat_partition": "train",
    "feat_scenario": 0,
    "target": 2,
    "text": "desperta'm a les nou a. m. del divendres",
    "feat_annot_utt": "desperta'm a les [time : nou a. m.] del [date : divendres]",
    "feat_worker_id": "42",
    "feat_slot_method.slot": [
      "time",
      "date"
    ],
    "feat_slot_method.method": [
      "translation",
      "translation"
    ],
    "feat_judgments.worker_id": [
      "42",
      "30",
      "3"
    ],
    "feat_judgments.intent_score": [
      1,
      1,
      1
    ],
    "feat_judgments.slots_score": [
      1,
      1,
      1
    ],
    "feat_judgments.grammar_score": [
      4,
      3,
      4
    ],
    "feat_judgments.spelling_score": [
      2,
      2,
      2
    ],
    "feat_judgments.language_identification": [
      "target",
      "target|english",
      "target"
    ]
  },
  {
    "feat_id": "2",
    "feat_locale": "ca-ES",
    "feat_partition": "train",
    "feat_scenario": 0,
    "target": 2,
    "text": "posa una alarma per d\u2019aqu\u00ed a dues hores",
    "feat_annot_utt": "posa una alarma per [time : d\u2019aqu\u00ed a dues hores]",
    "feat_worker_id": "15",
    "feat_slot_method.slot": [
      "time"
    ],
    "feat_slot_method.method": [
      "translation"
    ],
    "feat_judgments.worker_id": [
      "42",
      "30",
      "24"
    ],
    "feat_judgments.intent_score": [
      1,
      1,
      1
    ],
    "feat_judgments.slots_score": [
      1,
      1,
      1
    ],
    "feat_judgments.grammar_score": [
      4,
      4,
      4
    ],
    "feat_judgments.spelling_score": [
      2,
      2,
      2
    ],
    "feat_judgments.language_identification": [
      "target",
      "target",
      "target"
    ]
  }
]
```

### Dataset Fields

The dataset has the following fields (also called "features"):

```json
{
  "feat_id": "Value(dtype='string', id=None)",
  "feat_locale": "Value(dtype='string', id=None)",
  "feat_partition": "Value(dtype='string', id=None)",
  "feat_scenario": "ClassLabel(num_classes=18, names=['alarm', 'audio', 'calendar', 'cooking', 'datetime', 'email', 'general', 'iot', 'lists', 'music', 'news', 'play', 'qa', 'recommendation', 'social', 'takeaway', 'transport', 'weather'], id=None)",
  "target": "ClassLabel(num_classes=60, names=['alarm_query', 'alarm_remove', 'alarm_set', 'audio_volume_down', 'audio_volume_mute', 'audio_volume_other', 'audio_volume_up', 'calendar_query', 'calendar_remove', 'calendar_set', 'cooking_query', 'cooking_recipe', 'datetime_convert', 'datetime_query', 'email_addcontact', 'email_query', 'email_querycontact', 'email_sendemail', 'general_greet', 'general_joke', 'general_quirky', 'iot_cleaning', 'iot_coffee', 'iot_hue_lightchange', 'iot_hue_lightdim', 'iot_hue_lightoff', 'iot_hue_lighton', 'iot_hue_lightup', 'iot_wemo_off', 'iot_wemo_on', 'lists_createoradd', 'lists_query', 'lists_remove', 'music_dislikeness', 'music_likeness', 'music_query', 'music_settings', 'news_query', 'play_audiobook', 'play_game', 'play_music', 'play_podcasts', 'play_radio', 'qa_currency', 'qa_definition', 'qa_factoid', 'qa_maths', 'qa_stock', 'recommendation_events', 'recommendation_locations', 'recommendation_movies', 'social_post', 'social_query', 'takeaway_order', 'takeaway_query', 'transport_query', 'transport_taxi', 'transport_ticket', 'transport_traffic', 'weather_query'], id=None)",
  "text": "Value(dtype='string', id=None)",
  "feat_annot_utt": "Value(dtype='string', id=None)",
  "feat_worker_id": "Value(dtype='string', id=None)",
  "feat_slot_method.slot": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
  "feat_slot_method.method": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
  "feat_judgments.worker_id": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
  "feat_judgments.intent_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
  "feat_judgments.slots_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
  "feat_judgments.grammar_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
  "feat_judgments.spelling_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
  "feat_judgments.language_identification": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)"
}
```

### Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

| Split name   | Num samples         |
| ------------ | ------------------- |
| train        | 11514 |
| valid        | 2033 |