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crodri
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autotrain-data-massive-4-catalan

Tasks:
Text Classification
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Processed data from AutoTrain data processor ([2022-12-13 11:51 ]

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Files changed (8) hide show
  1. README.md +153 -0
  2. processed/dataset_dict.json +1 -0
  3. processed/train/dataset.arrow +3 -0
  4. processed/train/dataset_info.json +229 -0
  5. processed/train/state.json +31 -0
  6. processed/valid/dataset.arrow +3 -0
  7. processed/valid/dataset_info.json +229 -0
  8. processed/valid/state.json +31 -0
README.md ADDED
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+ ---
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+ task_categories:
3
+ - text-classification
4
+
5
+ ---
6
+ # AutoTrain Dataset for project: massive-4-catalan
7
+
8
+ ## Dataset Description
9
+
10
+ This dataset has been automatically processed by AutoTrain for project massive-4-catalan.
11
+
12
+ ### Languages
13
+
14
+ The BCP-47 code for the dataset's language is unk.
15
+
16
+ ## Dataset Structure
17
+
18
+ ### Data Instances
19
+
20
+ A sample from this dataset looks as follows:
21
+
22
+ ```json
23
+ [
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+ {
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+ "feat_id": "1",
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+ "feat_locale": "ca-ES",
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+ "feat_partition": "train",
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+ "feat_scenario": 0,
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+ "target": 2,
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+ "text": "desperta'm a les nou a. m. del divendres",
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+ "feat_annot_utt": "desperta'm a les [time : nou a. m.] del [date : divendres]",
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+ "feat_worker_id": "42",
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+ "feat_slot_method.slot": [
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+ "time",
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+ "date"
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+ ],
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+ "feat_slot_method.method": [
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+ "translation",
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+ "translation"
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+ ],
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+ "feat_judgments.worker_id": [
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+ "42",
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+ "30",
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+ "3"
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+ ],
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+ "feat_judgments.intent_score": [
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+ 1,
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+ 1,
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+ 1
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+ ],
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+ "feat_judgments.slots_score": [
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+ 1,
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+ 1,
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+ 1
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+ ],
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+ "feat_judgments.grammar_score": [
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+ 4,
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+ 3,
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+ 4
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+ ],
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+ "feat_judgments.spelling_score": [
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+ 2,
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+ 2,
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+ 2
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+ ],
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+ "feat_judgments.language_identification": [
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+ "target",
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+ "target|english",
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+ "target"
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+ ]
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+ },
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+ {
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+ "feat_id": "2",
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+ "feat_locale": "ca-ES",
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+ "feat_partition": "train",
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+ "feat_scenario": 0,
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+ "target": 2,
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+ "text": "posa una alarma per d\u2019aqu\u00ed a dues hores",
79
+ "feat_annot_utt": "posa una alarma per [time : d\u2019aqu\u00ed a dues hores]",
80
+ "feat_worker_id": "15",
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+ "feat_slot_method.slot": [
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+ "time"
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+ ],
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+ "feat_slot_method.method": [
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+ "translation"
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+ ],
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+ "feat_judgments.worker_id": [
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+ "42",
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+ "30",
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+ "24"
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+ ],
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+ "feat_judgments.intent_score": [
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+ 1,
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+ 1,
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+ 1
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+ ],
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+ "feat_judgments.slots_score": [
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+ 1,
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+ 1,
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+ 1
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+ ],
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+ "feat_judgments.grammar_score": [
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+ 4,
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+ 4,
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+ 4
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+ ],
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+ "feat_judgments.spelling_score": [
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+ 2,
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+ 2,
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+ 2
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+ ],
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+ "feat_judgments.language_identification": [
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+ "target",
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+ "target",
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+ "target"
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+ ]
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+ }
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+ ]
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+ ```
120
+
121
+ ### Dataset Fields
122
+
123
+ The dataset has the following fields (also called "features"):
124
+
125
+ ```json
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+ {
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+ "feat_id": "Value(dtype='string', id=None)",
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+ "feat_locale": "Value(dtype='string', id=None)",
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+ "feat_partition": "Value(dtype='string', id=None)",
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+ "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)",
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+ "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)",
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+ "text": "Value(dtype='string', id=None)",
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+ "feat_annot_utt": "Value(dtype='string', id=None)",
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+ "feat_worker_id": "Value(dtype='string', id=None)",
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+ "feat_slot_method.slot": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
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+ "feat_slot_method.method": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
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+ "feat_judgments.worker_id": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
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+ "feat_judgments.intent_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
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+ "feat_judgments.slots_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
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+ "feat_judgments.grammar_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
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+ "feat_judgments.spelling_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
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+ "feat_judgments.language_identification": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)"
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+ }
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+ ```
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+
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+ ### Dataset Splits
147
+
148
+ This dataset is split into a train and validation split. The split sizes are as follow:
149
+
150
+ | Split name | Num samples |
151
+ | ------------ | ------------------- |
152
+ | train | 11514 |
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+ | valid | 2033 |
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