Text Generation
Transformers
PyTorch
English
t5
text2text-generation
paraphrasing
transformer
text-generation-inference
Instructions to use SRDdev/Paraphrase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/Paraphrase with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SRDdev/Paraphrase")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SRDdev/Paraphrase") model = AutoModelForSeq2SeqLM.from_pretrained("SRDdev/Paraphrase") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SRDdev/Paraphrase with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SRDdev/Paraphrase" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SRDdev/Paraphrase", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SRDdev/Paraphrase
- SGLang
How to use SRDdev/Paraphrase 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 "SRDdev/Paraphrase" \ --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": "SRDdev/Paraphrase", "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 "SRDdev/Paraphrase" \ --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": "SRDdev/Paraphrase", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SRDdev/Paraphrase with Docker Model Runner:
docker model run hf.co/SRDdev/Paraphrase
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license: apache-2.0
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license: apache-2.0
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language: en
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tags:
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- text-generation
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- paraphrasing
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- transformer
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datasets:
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- SQUAD
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pipeline_tag: text2text-generation
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# Paraphraser Model Card
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## Model Details
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- **Model Name**: Paraphraser
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- **Model ID**: SRD/Paraphraser
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- **Author**: SRD
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- **Language**: English
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- **License**: Apache-2.0
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## Description
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The Paraphraser is a sequence-to-sequence model fine-tuned for paraphrasing sentences. It is built upon the T5 (Text-to-Text Transfer Transformer) architecture and aims to generate diverse paraphrases for a given input sentence.
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## Intended Use
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The primary purpose of this model is to assist users in generating paraphrases for input sentences. It can be utilized in various natural language processing tasks, including data augmentation, text generation, and content rewriting.
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## Limitations and Considerations
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- The quality of paraphrases may vary, and it is recommended to review generated outputs.
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- The model might produce paraphrases that are contextually incorrect or nonsensical.
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- Long sentences or complex language may result in less coherent paraphrases.
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- The model is sensitive to input phrasing, and slight rephrasing may lead to different outputs.
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## Training Data
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The model is trained on a SQUAD dataset composed of diverse sentences from various sources to ensure a broad understanding of language and context.
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