readme: ---
  datasets:
  - tweet_eval
  language:
  - en
  ---
  # Twitter-roBERTa-base for Sentiment Analysis
  
  This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see [XLM-T](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)).
  
  - Reference Paper: [_TweetEval_ (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). 
  - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
  
  <b>Labels</b>: 
  0 -> Negative;
  1 -> Neutral;
  2 -> Positive
  
  <b>New!</b> We just released a new sentiment analysis model trained on more recent and a larger quantity of tweets. 
  See [twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) and [TweetNLP](https://tweetnlp.org) for more details.
  
  ## Example of classification
  
  ```python
  from transformers import AutoModelForSequenceClassification
  from transformers import TFAutoModelForSequenceClassification
  from transformers import AutoTokenizer
  import numpy as np
  from scipy.special import softmax
  import csv
  import urllib.request
  
  # 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)
  
  # Tasks:
  # emoji, emotion, hate, irony, offensive, sentiment
  # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
  
  task='sentiment'
  MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
  
  tokenizer = AutoTokenizer.from_pretrained(MODEL)
  
  # download label mapping
  labels=[]
  mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
  with urllib.request.urlopen(mapping_link) as f:
      html = f.read().decode('utf-8').split("\n")
      csvreader = csv.reader(html, delimiter='\t')
  labels = [row[1] for row in csvreader if len(row) > 1]
  
  # 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)
  
  ranking = np.argsort(scores)
  ranking = ranking[::-1]
  for i in range(scores.shape[0]):
      l = labels[ranking[i]]
      s = scores[ranking[i]]
      print(f"{i+1}) {l} {np.round(float(s), 4)}")
  
  ```
  
  Output: 
  
  ```
  1) positive 0.8466
  2) neutral 0.1458
  3) negative 0.0076
  ```
  
  ### BibTeX entry and citation info
  
  Please cite the [reference paper](https://aclanthology.org/2020.findings-emnlp.148/) if you use this model.
  
  ```bibtex
  @inproceedings{barbieri-etal-2020-tweeteval,
      title = "{T}weet{E}val: Unified Benchmark and Comparative Evaluation for Tweet Classification",
      author = "Barbieri, Francesco  and
        Camacho-Collados, Jose  and
        Espinosa Anke, Luis  and
        Neves, Leonardo",
      booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
      month = nov,
      year = "2020",
      address = "Online",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/2020.findings-emnlp.148",
      doi = "10.18653/v1/2020.findings-emnlp.148",
      pages = "1644--1650"
  }
  ```