readme: ---
  language: multilingual
  widget:
  - text: "🤗🤗🤗<mask>"
  - text: "🔥The goal of life is <mask> . 🔥"
  - text: "Il segreto della vita è l’<mask> . ❤️"
  - text: "Hasta <mask> 👋!"
  ---
  
  
  # Twitter-XLM-Roberta-base
  This is a XLM-Roberta-base model trained on ~198M multilingual tweets, described and evaluated in the [reference paper](https://arxiv.org/abs/2104.12250). To evaluate this and other LMs on Twitter-specific data, please refer to the [main repository](https://github.com/cardiffnlp/xlm-t). A usage example is provided below. 
  
  ## Computing tweet similarity
  
  ```python
  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)
  
  def get_embedding(text):
      text = preprocess(text)
      encoded_input = tokenizer(text, return_tensors='pt')
      features = model(**encoded_input)
      features = features[0].detach().numpy() 
      features_mean = np.mean(features[0], axis=0) 
      return features_mean
  
  query = "Acabo de pedir pollo frito 🐣" #spanish
  
  tweets = ["We had a great time! ⚽️", # english
            "We hebben een geweldige tijd gehad! ⛩", # dutch
            "Nous avons passé un bon moment! 🎥", # french
            "Ci siamo divertiti! 🍝"] # italian
  
  d = defaultdict(int)
  for tweet in tweets:
      sim = 1-cosine(get_embedding(query),get_embedding(tweet))
      d[tweet] = sim
      
  print('Most similar to: ',query)
  print('----------------------------------------')
  for idx,x in enumerate(sorted(d.items(), key=lambda x:x[1], reverse=True)):
    print(idx+1,x[0])
  ```
  ```
  Most similar to:  Acabo de pedir pollo frito 🐣
  ----------------------------------------
  1 Ci siamo divertiti! 🍝
  2 Nous avons passé un bon moment! 🎥
  3 We had a great time! ⚽️
  4 We hebben een geweldige tijd gehad! ⛩
  ```