File size: 5,018 Bytes
5a8ac75 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
---
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 |
|