XLM-T-Sent-Politics

这是多语言twitter-xlm-roberta-base-sentiment模型（ model ， original paper ）的“扩展”，重点关注政治家推文的情感。原始情感微调是在8种语言（阿拉伯语，英语，法语，德语，印地语，意大利语，西班牙语，葡萄牙语）上进行的，但是后来使用来自英国议会成员（英语），西班牙议会成员（西班牙语）和希腊议会成员（希腊语）的推文进行了进一步的训练。

• 参考文献： Politics, Sentiment and Virality: A Large-Scale Multilingual Twitter Analysis in Greece, Spain and United Kingdom
• Git Repo： https://github.com/cardiffnlp/politics-and-virality-twitter

完整的分类示例

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

MODEL = f"cardiffnlp/xlm-twitter-politics-sentiment"

tokenizer = AutoTokenizer.from_pretrained(MODEL)

# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)

text = "Good night 😊"
text = preprocess(text)
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)
# model.save_pretrained(MODEL)

# text = "Good night 😊"
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)

# Print labels and scores
ranking = np.argsort(scores)
for i in range(scores.shape[0]):
    s = scores[ranking[i]]
    print(i, s)

输出：

0 0.0048229103
1 0.03117284
2 0.9640044