--- 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 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 ```