language:
- en
- es
- ja
- el
widget:
- text: It is great to see athletes promoting awareness for climate change.
datasets:
- cardiffnlp/tweet_topic_multi
- cardiffnlp/tweet_topic_multilingual
license: mit
metrics:
- f1
pipeline_tag: text-classification
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
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.", }