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---
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:
```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 0.8135 0.8565 8333
device_type 0.8502 0.8825 0.8661 11631
drink_type 0.0000 0.0000 0.0000 131
email_address 0.9715 0.9747 0.9731 3986
email_folder 0.5913 0.9740 0.7359 884
event_name 0.7659 0.7630 0.7645 38625
food_type 0.6502 0.8697 0.7441 12353
game_name 0.8974 0.6275 0.7386 4518
general_frequency 0.8012 0.8673 0.8329 3173
house_place 0.9337 0.9168 0.9252 7067
ingredient 0.5481 0.0491 0.0901 1161
joke_type 0.8147 0.9101 0.8598 1435
list_name 0.8411 0.7275 0.7802 8188
meal_type 0.6072 0.8926 0.7227 2282
media_type 0.8578 0.8522 0.8550 17751
movie_name 0.4598 0.1856 0.2645 431
movie_type 0.2673 0.4341 0.3309 364
music_album 0.0000 0.0000 0.0000 146
music_descriptor 0.2906 0.3979 0.3359 1053
music_genre 0.7999 0.7483 0.7732 5908
news_topic 0.7052 0.5702 0.6306 9265
order_type 0.6374 0.8845 0.7409 2614
person 0.8173 0.9376 0.8733 33708
personal_info 0.7035 0.7444 0.7234 1976
place_name 0.8616 0.8228 0.8417 38881
player_setting 0.6429 0.6212 0.6319 5409
playlist_name 0.5852 0.5293 0.5559 3671
podcast_descriptor 0.7486 0.5413 0.6283 4951
podcast_name 0.6858 0.5675 0.6211 3339
radio_name 0.8196 0.8013 0.8103 9892
relation 0.6662 0.8569 0.7496 6477
song_name 0.5617 0.7527 0.6433 7251
sport_type 0.0000 0.0000 0.0000 0
time 0.9032 0.8195 0.8593 35456
time_zone 0.8368 0.4467 0.5824 2823
timeofday 0.7931 0.8459 0.8187 6140
transport_agency 0.7876 0.7764 0.7820 1051
transport_descriptor 0.5738 0.2756 0.3723 254
transport_name 0.8497 0.5149 0.6412 1010
transport_type 0.9303 0.8980 0.9139 6363
weather_descriptor 0.8584 0.7466 0.7986 11702
accuracy 0.9092 1455270
macro avg 0.6940 0.6668 0.6613 1455270
weighted avg 0.9111 0.9092 0.9086 1455270
```
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