readme: ---
  language: en
  widget:
  - text: "As everybody knows Trump is by far the best USA president... XD"
  ---
  
  # T5-base fine-tuned for Sarcasm Detection 🙄
  [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [ Twitter Sarcasm Dataset](https://github.com/EducationalTestingService/sarcasm) for **Sequence classification (as text generation)** downstream task.
  
  ## Details of T5
  
  The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
  
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
  
  ![model image](https://i.imgur.com/jVFMMWR.png)
  ## Details of the downstream task (Sequence Classification as Text generation) - Dataset 📚
  
  [ Twitter Sarcasm Dataset](https://github.com/EducationalTestingService/sarcasm)
  
  
  For Twitter training and testing datasets are provided for sarcasm detection tasks in jsonlines format. 
  
  Each line contains a JSON object with the following fields : 
  - ***label*** : `SARCASM` or `NOT_SARCASM`  
  	- **NOT** in test data
  - ***id***:  String identifier for sample. This id will be required when making submissions.
  	- **ONLY** in test data
  - ***response*** :  the sarcastic response, whether a sarcastic Tweet
  - ***context*** : the conversation context of the ***response***
  	- Note, the context is an ordered list of dialogue, i.e., if the context contains three elements, `c1`, `c2`, `c3`, in that order, then `c2` is a reply to `c1` and `c3` is a reply to `c2`. Further, if the sarcastic response is `r`, then `r` is a reply to `c3`.
  
  For instance, for the following training example : 
  
  `"label": "SARCASM", "response": "Did Kelly just call someone else messy? Baaaahaaahahahaha", "context": ["X is looking a First Lady should . #classact, "didn't think it was tailored enough it looked messy"]`
  
  The response tweet, "Did Kelly..." is a reply to its immediate context "didn't think it was tailored..." which is a reply to "X is looking...". Your goal is to predict the label of the "response" while also using the context (i.e, the immediate or the full context).
  
  ***Dataset size statistics*** :
  
  |         | Train | Val  | Test |
  |---------|-------|------|------|
  | Twitter | 4050  | 450  | 500  |
  
  The datasets was preprocessed to convert it to a **text-to-text** (classfication as generation task).
  
  ## Model fine-tuning 🏋️‍
  
  The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him!
  
  ## Test set metrics 🧾
  
  |          | precision| recall  | f1-score |support|
  |----------|----------|---------|----------|-------|
  | derison  |    0.84  |   0.80  |    0.82  |  246  |
  | normal   |    0.82  |   0.85  |    0.83  |  254  | 
  |                                                  |
  |accuracy|            |         |      0.83|    500|
  |macro avg|       0.83|     0.83|      0.83|    500|
  |weighted avg|    0.83|     0.83|      0.83|    500|
      
  
  
  ## Model in Action 🚀
  
  ```python
  from transformers import AutoTokenizer, AutoModelWithLMHead
  tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-sarcasm-twitter")
  
  model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-sarcasm-twitter")
  
  def eval_conversation(text):
  
    input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
  
    output = model.generate(input_ids=input_ids, max_length=3)
    
    dec = [tokenizer.decode(ids) for ids in output]
  
    label = dec[0]
  
    return label
  
  # For similarity with the training dataset we should replace users mentions in twits for @USER token and urls for URL token.
  
  twit1 = "Trump just suspended the visa program that allowed me to move to the US to start @USER!" +
  " Unfortunately, I won’t be able to vote in a few months but