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cardiffnlp/
tweet-topic-base-multilingual

Name: tweet-topic-base-multilingual
Availability: InStock

safetensors

xlm-roberta

text-classification

cardiffnlp/tweet_topic_multi

cardiffnlp/tweet_topic_multilingual

2410.03075

mit

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tweet-topic-base-multilingual

This model is based on cardiffnlp/twitter-xlm-roberta-base language model trained rained on ~198M multilingual tweets and finetuned for multi-label topic classification in English, Spanish, Japanese, and Greek.

The models is trained using TweetTopic and X-Topic datasets (see main EMNLP 2024 reference paper).

Labels:

0: arts_&_culture	5: fashion_&_style	10: learning_&_educational	15: science_&_technology
1: business_&_entrepreneurs	6: film_tv_&_video	11: music	16: sports
2: celebrity_&_pop_culture	7: fitness_&_health	12: news_&_social_concern	17: travel_&_adventure
3: diaries_&_daily_life	8: food_&_dining	13: other_hobbies	18: youth_&_student_life
4: family	9: gaming	14: relationships

Full classification example

python

from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import expit

    
MODEL = f"cardiffnlp/tweet-topic-base-multilingual"
tokenizer = AutoTokenizer.from_pretrained(MODEL)

# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
class_mapping = model.config.id2label

text = "It is great to see athletes promoting awareness for climate change."
tokens = tokenizer(text, return_tensors='pt')
output = model(**tokens)

scores = output[0][0].detach().numpy()
scores = expit(scores)
predictions = (scores >= 0.5) * 1


# TF
#tf_model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
#class_mapping = tf_model.config.id2label
#text = "It is great to see athletes promoting awareness for climate change."
#tokens = tokenizer(text, return_tensors='tf')
#output = tf_model(**tokens)
#scores = output[0][0]
#scores = expit(scores)
#predictions = (scores >= 0.5) * 1

# Map to classes
for i in range(len(predictions)):
  if predictions[i]:
    print(class_mapping[i])

Output:

news_&_social_concern
sports

Results on X-Topic

	English	Spanish	Japanese	Greek
Macro-F1	55.4	48.5	50.8	41.3
Micro-F1	63.5	63.3	57.8	69.8

BibTeX entry and citation info

@inproceedings{antypas-etal-2024-multilingual, title = "Multilingual Topic Classification in {X}: Dataset and Analysis", author = "Antypas, Dimosthenis and Ushio, Asahi and Barbieri, Francesco and Camacho-Collados, Jose", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.1123", pages = "20136--20152", abstract = "In the dynamic realm of social media, diverse topics are discussed daily, transcending linguistic boundaries. However, the complexities of understanding and categorising this content across various languages remain an important challenge with traditional techniques like topic modelling often struggling to accommodate this multilingual diversity. In this paper, we introduce X-Topic, a multilingual dataset featuring content in four distinct languages (English, Spanish, Japanese, and Greek), crafted for the purpose of tweet topic classification. Our dataset includes a wide range of topics, tailored for social media content, making it a valuable resource for scientists and professionals working on cross-linguistic analysis, the development of robust multilingual models, and computational scientists studying online dialogue. Finally, we leverage X-Topic to perform a comprehensive cross-linguistic and multilingual analysis, and compare the capabilities of current general- and domain-specific language models.", }