crodri/autotrain-data-massive-4-catalan

---
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 |

原始信息汇总

数据集概述

数据集名称

• AutoTrain Dataset for project: massive-4-catalan

任务类别

• text-classification

语言信息

• BCP-47代码：unk

数据集结构

数据实例

• 示例数据包含多个字段，如feat_id, feat_locale, feat_partition等。
• 文本示例："despertam a les nou a. m. del divendres"

数据集字段

• feat_id: 字符串类型
• feat_locale: 字符串类型
• feat_partition: 字符串类型
• feat_scenario: 分类标签，18个类别
• target: 分类标签，60个类别
• text: 字符串类型
• feat_annot_utt: 字符串类型
• feat_worker_id: 字符串类型
• feat_slot_method.slot: 序列类型，字符串特征
• feat_slot_method.method: 序列类型，字符串特征
• feat_judgments.worker_id: 序列类型，字符串特征
• feat_judgments.intent_score: 序列类型，整数特征
• feat_judgments.slots_score: 序列类型，整数特征
• feat_judgments.grammar_score: 序列类型，整数特征
• feat_judgments.spelling_score: 序列类型，整数特征
• feat_judgments.language_identification: 序列类型，字符串特征

数据集分割

• 训练集：11514样本
• 验证集：2033样本