cardiffnlp / tweet-topic-latest-single

tweet-topic-latest-single

This is a RoBERTa-base model trained on 168.86M tweets until the end of September 2022 and finetuned for single-label topic classification on a corpus of 6,997 tweets . The original RoBERTa-base model can be found here . This model is suitable for English.

• Reference Papers: TimeLMs paper , TweetTopic
• Git Repo: TimeLMs official repository .

Labels :

• 0 -> arts_&_culture;
• 1 -> business_&_entrepreneurs;
• 2 -> pop_culture;
• 3 -> daily_life;
• 4 -> sports_&_gaming;
• 5 -> science_&_technology

Full classification example

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

    
MODEL = f"cardiffnlp/tweet-topic-latest-single"
tokenizer = AutoTokenizer.from_pretrained(MODEL)

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

text = "Tesla stock is on the rise!"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

scores = output[0][0].detach().numpy()
scores = softmax(scores)

# TF
#model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
#class_mapping = model.config.id2label
#text = "Tesla stock is on the rise!"
#encoded_input = tokenizer(text, return_tensors='tf')
#output = model(**encoded_input)
#scores = output[0][0]
#scores = softmax(scores)


ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
    l = class_mapping[ranking[i]]
    s = scores[ranking[i]]
    print(f"{i+1}) {l} {np.round(float(s), 4)}")

Output:

1) business_&_entrepreneurs 0.8929
2) sports_&_gaming 0.0478
3) science_&_technology 0.0185
4) daily_life 0.0178
5) arts_&_culture 0.0128
6) pop_culture 0.0102