Instructions to use hetpandya/t5-small-tapaco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hetpandya/t5-small-tapaco with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hetpandya/t5-small-tapaco") model = AutoModelForSeq2SeqLM.from_pretrained("hetpandya/t5-small-tapaco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: en | |
| datasets: | |
| - tapaco | |
| # T5-small for paraphrase generation | |
| Google's T5 small fine-tuned on [TaPaCo](https://huggingface.co/datasets/tapaco) dataset for paraphrasing. | |
| ## Model in Action 🚀 | |
| ```python | |
| from transformers import T5ForConditionalGeneration, T5Tokenizer | |
| tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-small-tapaco") | |
| model = T5ForConditionalGeneration.from_pretrained("hetpandya/t5-small-tapaco") | |
| def get_paraphrases(sentence, prefix="paraphrase: ", n_predictions=5, top_k=120, max_length=256,device="cpu"): | |
| text = prefix + sentence + " </s>" | |
| encoding = tokenizer.encode_plus( | |
| text, pad_to_max_length=True, return_tensors="pt" | |
| ) | |
| input_ids, attention_masks = encoding["input_ids"].to(device), encoding[ | |
| "attention_mask" | |
| ].to(device) | |
| model_output = model.generate( | |
| input_ids=input_ids, | |
| attention_mask=attention_masks, | |
| do_sample=True, | |
| max_length=max_length, | |
| top_k=top_k, | |
| top_p=0.98, | |
| early_stopping=True, | |
| num_return_sequences=n_predictions, | |
| ) | |
| outputs = [] | |
| for output in model_output: | |
| generated_sent = tokenizer.decode( | |
| output, skip_special_tokens=True, clean_up_tokenization_spaces=True | |
| ) | |
| if ( | |
| generated_sent.lower() != sentence.lower() | |
| and generated_sent not in outputs | |
| ): | |
| outputs.append(generated_sent) | |
| return outputs | |
| paraphrases = get_paraphrases("The house will be cleaned by me every Saturday.") | |
| for sent in paraphrases: | |
| print(sent) | |
| ``` | |
| ## Output | |
| ``` | |
| The house is cleaned every Saturday by me. | |
| The house will be cleaned on Saturday. | |
| I will clean the house every Saturday. | |
| I get the house cleaned every Saturday. | |
| I will clean this house every Saturday. | |
| ``` | |
| ## Model fine-tuning | |
| Please find my guide on fine-tuning the model here: | |
| https://towardsdatascience.com/training-t5-for-paraphrase-generation-ab3b5be151a2 | |
| Created by [Het Pandya/@hetpandya](https://github.com/hetpandya) | [LinkedIn](https://www.linkedin.com/in/het-pandya) | |
| Made with <span style="color: red;">♥</span> in India |