readme: # Twitter-roBERTa-base for Irony Detection
  
  This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.
  
  - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). 
  - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
  
  ## 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='irony'
  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 = "Great, it broke the first day..."
  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 = "Great, it broke the first day..."
  # 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) irony 0.914
  2) non_irony 0.086
  ```
  