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---
language:
- multilingual
- en
- pt
- es
- ar
- ko
- ja
- id
- tl
- tr
- fr
- ru
- th
- it
- de
- fa
- pl
- hi
- nl
- ht
- et
- ud
- ca
- sv
- fi
- el
- cs
- eu
- he
- ta
- zh
- no
- da
- cy
- lv
- hu
- ro
- lt
- vi
- uk
- ne
- sl
- is
- sr
- ml
- bn
- bg
- mr
- si
- te
- kn
- ku
- ps
- gu
- my
- am
- hy
- or
- sd
- pa
- km
- ka
- lo
- dv
- ug
widget:
- text: "๐Ÿค—"
- text: "T'estimo! โค๏ธ"
- text: "I love you!"
- text: "I hate you ๐Ÿคฎ"
- text: "Mahal kita!"
- text: "์‚ฌ๋ž‘ํ•ด!"
- text: "๋‚œ ๋„ˆ๊ฐ€ ์‹ซ์–ด"
- text: "๐Ÿ˜๐Ÿ˜๐Ÿ˜"
---
# twitter-XLM-roBERTa-base for Sentiment Analysis
This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).
- Paper: [XLM-T: A Multilingual Language Model Toolkit for Twitter](https://arxiv.org/abs/2104.12250).
- Git Repo: [XLM-T official repository](https://github.com/cardiffnlp/xlm-t).
## Example Pipeline
```python
from transformers import pipeline
model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
sentiment_task("T'estimo!")
```
```
[{'label': 'Positive', 'score': 0.6600581407546997}]
```
## Full classification example
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
import numpy as np
from scipy.special import softmax
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
MODEL = f"cardiffnlp/twitter-xlm-roberta-base-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "Good night ๐Ÿ˜Š"
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 ๐Ÿ˜Š"
# 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)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = config.id2label[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
Output:
```
1) Positive 0.7673
2) Neutral 0.2015
3) Negative 0.0313
```