tweet-topic-latest-single

这是一个基于RoBERTa-base的模型，训练于2022年9月底之前的1.6886亿个推文，并在6,997个语料库上进行了单标签主题分类的微调。原始的RoBERTa-base模型可以在此处找到。该模型适用于英文。

• 参考论文： TimeLMs paper ， TweetTopic
• Git存储库： TimeLMs official repository

标签：

• 0 -> 艺术与文化;
• 1 -> 商业与企业家;
• 2 -> 流行文化;
• 3 -> 日常生活;
• 4 -> 体育与游戏;
• 5 -> 科学与技术

完整的分类示例

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)}")

输出：

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