
# 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
```

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "XLM-T-Sent-Politics", "results": []}]}
transformers pytorch tf xlm-roberta text-classification generated_from_keras_callback arxiv:2104.12250 arxiv:2202.00396 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #generated_from_keras_callback #arxiv-2104.12250 #arxiv-2202.00396 #autotrain_compatible #endpoints_compatible #region-us 


# XLM-T-Sent-Politics

This is an "extension" of the multilingual 'twitter-xlm-roberta-base-sentiment' model (model, original paper) 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. 
- Git Repo: URL


## Full classification example



Output: 



# XLM-T-Sent-Politics

This is an "extension" of the multilingual 'twitter-xlm-roberta-base-sentiment' model (model, original paper) 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. 
- Git Repo: URL ## Full classification example



Output:
TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #generated_from_keras_callback #arxiv-2104.12250 #arxiv-2202.00396 #autotrain_compatible #endpoints_compatible #region-us 
 # XLM-T-Sent-Politics

This is an "extension" of the multilingual 'twitter-xlm-roberta-base-sentiment' model (model, original paper) 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. 
- Git Repo: URL ## Full classification example



Output:
passage: TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #generated_from_keras_callback #arxiv-2104.12250 #arxiv-2202.00396 #autotrain_compatible #endpoints_compatible #region-us 
# XLM-T-Sent-Politics

This is an "extension" of the multilingual 'twitter-xlm-roberta-base-sentiment' model (model, original paper) 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. 
- Git Repo: URL## Full classification example



Output: