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---
tags:
- generated_from_keras_callback
model-index:
- name: XLM-T-Sent-Politics
results: []
---
# XLM-T-Sent-Politics
This is an "extension" of the multilingual `twitter-xlm-roberta-base-sentiment` model ([model](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment), [original paper](https://arxiv.org/abs/2104.12250)) with a focus on sentiment from politicians' tweets. The original sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but further training was done using tweets from Members of Parliament from UK (English), Spain (Spanish) and Greece (Greek).
- Reference Paper: [Politics, Sentiment and Virality: A Large-Scale Multilingual Twitter Analysis in Greece, Spain and United Kingdom](https://arxiv.org/pdf/2202.00396.pdf).
- Git Repo: [https://github.com/cardiffnlp/politics-and-virality-twitter](https://github.com/cardiffnlp/politics-and-virality-twitter).
## Full classification example
```python
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)
```
Output:
```
0 0.0048229103
1 0.03117284
2 0.9640044
```
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