readme: ---
  tags:
  - Transformers
  - text-classification
  - intent-classification
  - multi-class-classification
  - natural-language-understanding
  languages:
  - af-ZA
  - am-ET
  - ar-SA
  - az-AZ
  - bn-BD
  - cy-GB
  - da-DK
  - de-DE
  - el-GR
  - en-US
  - es-ES
  - fa-IR
  - fi-FI
  - fr-FR
  - he-IL
  - hi-IN
  - hu-HU
  - hy-AM
  - id-ID
  - is-IS
  - it-IT
  - ja-JP
  - jv-ID
  - ka-GE
  - km-KH
  - kn-IN
  - ko-KR
  - lv-LV
  - ml-IN
  - mn-MN
  - ms-MY
  - my-MM
  - nb-NO
  - nl-NL
  - pl-PL
  - pt-PT
  - ro-RO
  - ru-RU
  - sl-SL
  - sq-AL
  - sv-SE
  - sw-KE
  - ta-IN
  - te-IN
  - th-TH
  - tl-PH
  - tr-TR
  - ur-PK
  - vi-VN
  - zh-CN
  - zh-TW
  multilinguality:
  - af-ZA
  - am-ET
  - ar-SA
  - az-AZ
  - bn-BD
  - cy-GB
  - da-DK
  - de-DE
  - el-GR
  - en-US
  - es-ES
  - fa-IR
  - fi-FI
  - fr-FR
  - he-IL
  - hi-IN
  - hu-HU
  - hy-AM
  - id-ID
  - is-IS
  - it-IT
  - ja-JP
  - jv-ID
  - ka-GE
  - km-KH
  - kn-IN
  - ko-KR
  - lv-LV
  - ml-IN
  - mn-MN
  - ms-MY
  - my-MM
  - nb-NO
  - nl-NL
  - pl-PL
  - pt-PT
  - ro-RO
  - ru-RU
  - sl-SL
  - sq-AL
  - sv-SE
  - sw-KE
  - ta-IN
  - te-IN
  - th-TH
  - tl-PH
  - tr-TR
  - ur-PK
  - vi-VN
  - zh-CN
  - zh-TW
  datasets:
  - qanastek/MASSIVE
  widget:
  - text: "wake me up at five am this week"
  - text: "je veux écouter la chanson de jacques brel encore une fois"
  - text: "quiero escuchar la canción de arijit singh una vez más"
  - text: "olly onde é que á um parque por perto onde eu possa correr"
  - text: "פרק הבא בפודקאסט בבקשה"
  - text: "亚马逊股价"
  - text: "найди билет на поезд в санкт-петербург"
  license: cc-by-4.0
  ---
  
  **People Involved**
  
  * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1)
  
  **Affiliations**
  
  1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France.
  
  ## Demo: How to use in HuggingFace Transformers Pipeline
  
  Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers```
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
  
  model_name = 'qanastek/XLMRoberta-Alexa-Intents-Classification'
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  model = AutoModelForSequenceClassification.from_pretrained(model_name)
  classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
  
  res = classifier("réveille-moi à neuf heures du matin le vendredi")
  print(res)
  ```
  
  Outputs:
  
  ```python
  [{'label': 'alarm_set', 'score': 0.9998375177383423}]
  ```
  
  ## Training data
  
  [MASSIVE](https://huggingface.co/datasets/qanastek/MASSIVE) is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.
  
  ## Intents
  
  * audio_volume_other
  * play_music
  * iot_hue_lighton
  * general_greet
  * calendar_set
  * audio_volume_down
  * social_query
  * audio_volume_mute
  * iot_wemo_on
  * iot_hue_lightup
  * audio_volume_up
  * iot_coffee
  * takeaway_query
  * qa_maths
  * play_game
  * cooking_query
  * iot_hue_lightdim
  * iot_wemo_off
  * music_settings
  * weather_query
  * news_query
  * alarm_remove
  * social_post
  * recommendation_events
  * transport_taxi
  * takeaway_order
  * music_query
  * calendar_query
  * lists_query
  * qa_currency
  * recommendation_movies
  * general_joke
  * recommendation_locations
  * email_querycontact
  * lists_remove
  * play_audiobook
  * email_addcontact
  * lists_createoradd
  * play_radio
  * qa_stock
  * alarm_query
  * email_sendemail
  * general_quirky
  * music_likeness
  * cooking_recipe
  * email_query
  * datetime_query
  * transport_traffic
  * play_podcasts
  * iot_hue_lightchange
  * calendar_remove
  * transport_query
  * transport_ticket
  * qa_factoid
  * iot_cleaning
  * alarm_set
  * datetime_convert
  * iot_hue_lightoff
  * qa_definition
  * music_dislikeness
  
  ## Evaluation results
  
  ```plain
                            precision    recall  f1-score   support
  
               alarm_query     0.9661    0.9037    0.9338      1734
              alarm_remove     0.9484    0.9608    0.9545      1071
                 alarm_set     0.8611    0.9254    0.8921      2091
         audio_volume_down     0.8657    0.9537    0.9075       561
         audio_volume_mute     0.8608    0.9130    0.8861      1632
        audio_volume_other     0.8684    0.5392    0.6653       306
           audio_volume_up     0.7198    0.8446    0.7772       663
            calendar_query     0.7555    0.8229    0.7878      6426
           calendar_remove     0.8688    0.9441    0.9049      3417
              calendar_set     0.9092    0.9014    0.9053     10659
             cooking_query     0.0000    0.0000    0.0000         0
            cooking_recipe     0.9282    0.8592    0.8924      3672
          datetime_convert     0.8144    0.7686    0.7909       765
            datetime_query     0.9152    0.9305    0.9228      4488
          email_addcontact     0.6482    0.8431    0.7330       612
               email_query     0.9629    0.9319    0.9472      6069
        email_querycontact     0.6853    0.8032    0.7396      1326
           email_sendemail     0.9530    0.9381    0.9455 