Create README.md

README.md

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+### 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 here](https://arxiv.org/abs/2202.05690)
+# How to use
+```python
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
+model = T5ForConditionalGeneration.from_pretrained("sana-ngu/HaT5_augmentation ")
+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)
+```