readme: ### HaT5(T5-base)
  This is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model for identifying Tweets (0 - HOF(hate/offensive); 1 - NOT). The task prefix we used for the T5 model is 'classification: '.
  
  More information about the original pre-trained model can be found [here](https://huggingface.co/t5-base)
  
  Classification examples:
  
  |Prediction|Tweet|
  |-----|--------|
  |0    |Why the fuck I got over 1000 views on my story 😂😂 nothing new over here      |
  |1.    |first of all there is no vaccine to cure , whthr it is capsules, tablets, or injections, they just support to fight with d virus. I do not support people taking any kind of home remedies n making fun of an ayurvedic medicine..😐     |
  # More Details 
  
  For more details  about the datasets and eval results, see [our paper for this work here](https://arxiv.org/abs/2202.05690)
  The paper was accepted at the International Joint Conference on Neural Networks (IJCNN) conference 2022.
  # How to use
  ```python
  
  from transformers import T5ForConditionalGeneration, T5Tokenizer
  import torch
  model = T5ForConditionalGeneration.from_pretrained("sana-ngu/HaT5")
  tokenizer = T5Tokenizer.from_pretrained("t5-base") 
  tokenizer.pad_token = tokenizer.eos_token
  input_ids = tokenizer("Old lions in the wild lay down and die with dignity when they can't hunt anymore. If a government is having 'teething problems' handling aid supplies one full year into a pandemic, maybe it should take a cue and get the fuck out of the way? ", padding=True, truncation=True, return_tensors='pt').input_ids
  outputs = model.generate(input_ids)
  pred = tokenizer.decode(outputs[0], skip_special_tokens=True)
  print(pred)
  
  ```
  