readme: ---
  tags:
  - Transformers
  - Token Classification
  - Slot Annotation
  - token-classification
  - sequence-tagger-model
  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, AutoModelForTokenClassification, TokenClassificationPipeline
  
  tokenizer = AutoTokenizer.from_pretrained('qanastek/XLMRoberta-Alexa-Intents-NER-NLU')
  model = AutoModelForTokenClassification.from_pretrained('qanastek/XLMRoberta-Alexa-Intents-NER-NLU')
  predict = TokenClassificationPipeline(model=model, tokenizer=tokenizer)
  res = predict("réveille-moi à neuf heures du matin le vendredi")
  print(res)
  ```
  
  Outputs:
  
  ![English - Hebrew - Spanish](123.png)
  
  ```python
  [{'word': '▁neuf', 'score': 0.9911066293716431, 'entity': 'B-time', 'index': 6, 'start': 15, 'end': 19},
  {'word': '▁heures', 'score': 0.9200698733329773, 'entity': 'I-time', 'index': 7, 'start': 20, 'end': 26},
  {'word': '▁du', 'score': 0.8476170897483826, 'entity': 'I-time', 'index': 8, 'start': 27, 'end': 29},
  {'word': '▁matin', 'score': 0.8271021246910095, 'entity': 'I-time', 'index': 9, 'start': 30, 'end': 35},
  {'word': '▁vendredi', 'score': 0.9813069701194763, 'entity': 'B-date', 'index': 11, 'start': 39, 'end': 47}]
  ```
  
  ## 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.
  
  ## Named Entities
  
  * O
  * currency_name
  * personal_info
  * app_name
  * list_name
  * alarm_type
  * cooking_type
  * time_zone
  * media_type
  * change_amount
  * transport_type
  * drink_type
  * news_topic
  * artist_name
  * weather_descriptor
  * transport_name
  * player_setting
  * email_folder
  * music_album
  * coffee_type
  * meal_type
  * song_name
  * date
  * movie_type
  * movie_name
  * game_name
  * business_type
  * music_descriptor
  * joke_type
  * music_genre
  * device_type
  * house_place
  * place_name
  * sport_type
  * podcast_name
  * game_type
  * timeofday
  * business_name
  * time
  * definition_word
  * audiobook_author
  * event_name
  * general_frequency
  * relation
  * color_type
  * audiobook_name
  * food_type
  * person
  * transport_agency
  * email_address
  * podcast_descriptor
  * order_type
  * ingredient
  * transport_descriptor
  * playlist_name
  * radio_name
  
  ## Evaluation results
  
  ```plain
                        precision    recall  f1-score   support
  
                     O     0.9537    0.9498    0.9517   1031927
            alarm_type     0.8214    0.1800    0.2953       511
              app_name     0.3448    0.5318    0.4184       660
           artist_name     0.7143    0.8487    0.7757     11413
      audiobook_author     0.7038    0.2971    0.4178      1232
        audiobook_name     0.7271    0.5381    0.6185      5090
         business_name     0.8301    0.7862    0.8075     15385
         business_type     0.7009    0.6196    0.6577      4600
         change_amount     0.8179    0.9104    0.8617      1663
           coffee_type     0.6147    0.8322    0.7071       876
            color_type     0.6999    0.9176    0.7941      2890
          cooking_type     0.7037    0.5184    0.5970      1003
         currency_name     0.8479    0.9686    0.9042      6501
                  date     0.8667    0.9348    0.8995     49866
       definition_word     0.9043   