Update README.md

README.md

@@ -1,16 +1,26 @@
 ### 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: '.
 
-The dataset it's trained on is limited in scope, as it covers only some news texts covering about 20 English-speaking countries. The macro F1 score achieved on the test set, based on the official evaluation, is 0.5452. More information about the original pre-trained model can be found here
+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..😐     |
+
+# How to use
+```python
+
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 import torch
 model = T5ForConditionalGeneration.from_pretrained("sana-ngu/HaT5")
-tokenizer = T5Tokenizer.from_pretrained("t5-base") # use the source tokenizer because T5 finetuned tokenizer breaks
+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)
+print(pred)
+
+```