tweet-topic-latest-multi

This is a RoBERTa-base model trained on 168.86M tweets until the end of September 2022 and finetuned for multi-label topic classification on a corpus of 11,267 tweets. The original RoBERTa-base model can be found here. This model is suitable for English.

<b>Labels</b>:

<span style="font-weight:normal">0: arts_&_culture</span> <span style="font-weight:normal">5: fashion_&_style</span> <span style="font-weight:normal">10: learning_&_educational</span> <span style="font-weight:normal">15: science_&_technology</span>
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-latest-multi"
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:

fitness_&_health
news_&_social_concern
sports