readme: ---
  language: multilingual
  widget:
  - text: "🤗"
  - text: "T'estimo! ❤️"
  - text: "I love you!"
  - text: "I hate you 🤮"
  - text: "Mahal kita!"
  - text: "사랑해!"
  - text: "난 너가 싫어"
  - text: "😍😍😍"
  ---
  
  
  # 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 😊"
  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)
  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
  ```
  
  