readme: ---
  language: en
  widget:
  - text: Covid cases are increasing fast!
  datasets:
  - tweet_eval
  ---
  
  
  # Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
  
  This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. 
  The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) and the original reference paper is [TweetEval](https://github.com/cardiffnlp/tweeteval). This model is suitable for English. 
  
  - Reference Paper: [TimeLMs paper](https://arxiv.org/abs/2202.03829). 
  - Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).
  
  <b>Labels</b>: 
  0 -> Negative;
  1 -> Neutral;
  2 -> Positive
  
  This sentiment analysis model has been integrated into [TweetNLP](https://github.com/cardiffnlp/tweetnlp). You can access the demo [here](https://tweetnlp.org).
  
  ## Example Pipeline
  ```python
  from transformers import pipeline
  sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
  sentiment_task("Covid cases are increasing fast!")
  ```
  ```
  [{'label': 'Negative', 'score': 0.7236}]
  ```
  
  ## 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-roberta-base-sentiment-latest"
  tokenizer = AutoTokenizer.from_pretrained(MODEL)
  config = AutoConfig.from_pretrained(MODEL)
  # PT
  model = AutoModelForSequenceClassification.from_pretrained(MODEL)
  #model.save_pretrained(MODEL)
  text = "Covid cases are increasing fast!"
  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 = "Covid cases are increasing fast!"
  # 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) Negative 0.7236
  2) Neutral 0.2287
  3) Positive 0.0477
  ```