This model re-fine-tunes the [ChatGPT Paraphraser on T5 Base](https://huggingface.co/humarin/chatgpt_paraphraser_on_T5_base) with additional Google PAWS dataset.

## Usage example
```python
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM

#'cuda' for gpu otherwise use 'cpu'
device = "cuda"
model     = AutoModelForSeq2SeqLM.from_pretrained("sharad/ParaphraseGPT").to(device)
tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
predict   = pipeline("text2text-generation", model=model, tokenizer=tokenizer)

def paraphrase(sentence):
  generated = predict(
              sentence,
              num_beams=3,
              num_beam_groups=3,
              num_return_sequences=1,
              diversity_penalty=2.0,
              no_repeat_ngram_size=2,
              repetition_penalty=0.99,
              max_length=len(sentence)
          )
  return generated

output = paraphrase('My sentence to paraphrase...')
print(output[0]['generated_text'])
```

## Train parameters
```python
epochs = 4
max_length = 128
lr = 5e-5
```
{"language": ["en"], "license": "apache-2.0", "tags": ["paraphrase", "similar text"], "datasets": ["humarin/chatgpt-paraphrases"]}
transformers pytorch t5 text2text-generation paraphrase similar text en dataset:humarin/chatgpt-paraphrases license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #paraphrase #similar text #en #dataset-humarin/chatgpt-paraphrases #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

This model re-fine-tunes the ChatGPT Paraphraser on T5 Base with additional Google PAWS dataset.

## Usage example


## Train parameters

## Usage example ## Train parameters
TAGS
#transformers #pytorch #t5 #text2text-generation #paraphrase #similar text #en #dataset-humarin/chatgpt-paraphrases #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ## Usage example ## Train parameters