predict.py

from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline

classes = ['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']

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)
# idx = int(res[0]["label"].split("_")[-1])
# print(idx)
# print(classes[idx])