tweet-topic-large-multilingual

This model is based on cardiffnlp/twitter-xlm-roberta-large-2022 language model and isfinetuned 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-large-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	60.2	52.9	57.3	50.3
Micro-F1	66.3	67.0	61.4	73.0

BibTeX entry and citation info

TBA