Text Generation
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
t5
text2text-generation
paraphrase-generation
Conditional Generation
text-generation-inference
Instructions to use Vamsi/T5_Paraphrase_Paws with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vamsi/T5_Paraphrase_Paws with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Vamsi/T5_Paraphrase_Paws")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws") model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Vamsi/T5_Paraphrase_Paws with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vamsi/T5_Paraphrase_Paws" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vamsi/T5_Paraphrase_Paws", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Vamsi/T5_Paraphrase_Paws
- SGLang
How to use Vamsi/T5_Paraphrase_Paws with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Vamsi/T5_Paraphrase_Paws" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vamsi/T5_Paraphrase_Paws", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Vamsi/T5_Paraphrase_Paws" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vamsi/T5_Paraphrase_Paws", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Vamsi/T5_Paraphrase_Paws with Docker Model Runner:
docker model run hf.co/Vamsi/T5_Paraphrase_Paws
Paraphrase-Generation
β
Model description
β T5 Model for generating paraphrases of english sentences. Trained on the Google PAWS dataset. β
How to use
β## Requires sentencepiece: # !pip install sentencepiece PyTorch and TF models available β
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
β
tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws").to('cuda')
β
sentence = "This is something which i cannot understand at all"
text = "paraphrase: " + sentence + " </s>"
encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=256,
do_sample=True,
top_k=120,
top_p=0.95,
early_stopping=True,
num_return_sequences=5
)
for output in outputs:
line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
print(line)
β
For more reference on training your own T5 model or using this model, do check out Paraphrase Generation.
- Downloads last month
- 109,697