Hupd T5 Small

🚀 HUPD T5-Small Summarization Model

This HUPD T5-Small model is fine-tuned for text summarization on the HUPD dataset, offering efficient and accurate summarization capabilities.

This HUPD T5-Small summarization model was fine-tuned on the HUPD dataset. It was originally introduced in this paper.

For more information about the Harvard USPTO Patent Dataset, please feel free to visit the project website or the project's GitHub repository.

🚀 Quick Start

✨ Features

• Fine-tuned on HUPD: Specifically optimized for summarization tasks using the HUPD dataset.
• Easy to Use: Can be directly integrated into existing pipelines for masked language modeling.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

You can use this model directly with a pipeline for masked language modeling:

from transformers import pipeline
summarizer = pipeline(task="summarization", model="HUPD/hupd-t5-small")

TEXT = "1. An optical coherent receiver for an optical communication network, said optical coherent receiver being configured to receive a modulated optical signal and to process said modulated optical signal for generating an in-phase component and a quadrature component, said in-phase component and said quadrature component being electrical signals, said optical coherent receiver comprising a power adjuster in turn comprising: a multiplying unit configured to multiply said in-phase component by an in-phase gain thereby providing a power-adjusted in-phase component, and to multiply said quadrature component by a quadrature gain thereby providing a power-adjusted quadrature component; and a digital circuit connected between output and input of said multiplying unit and configured to compute: a common gain indicative of a sum of a power of said power-adjusted in-phase component and a power of said power-adjusted quadrature component, and a differential gain indicative of a difference between said power of said power-adjusted in-phase component and said power of said power-adjusted quadrature component; and said in-phase gain as a product between said common gain and said differential gain, and said quadrature gain as a ratio between said common gain and said differential gain. 2. An optical coherent receiver according to claim 1, wherein it further comprises an analog-to-digital unit connected at the input of said power adjuster, said analog-to-digital unit being configured to ..."

summarizer(TEXT)

Here is the output:

[{'summary_text': 'An optical coherent receiver for an optical communication network includes a power adjuster and a digital circuit connected between output and input of the multiplying unit and configured to compute a common gain indicative of a sum of the power of an in-phase component and the power-adjusted quadrature component, and the differential gain as a product between the common gain and the diffractive gain.'}]

Advanced Usage

Alternatively, you can load the model and use it as follows:

import torch
from transformers import AutoTokenizer, AutoModelWithLMHead
# cuda/cpu
device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = AutoTokenizer.from_pretrained("HUPD/hupd-t5-small")
model = AutoModelWithLMHead.from_pretrained("HUPD/hupd-t5-small").to(device)

inputs = tokenizer(TEXT, return_tensors="pt").to(device)

with torch.no_grad():
    outputs = model.generate(inputs.input_ids, max_new_tokens=256)

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

📚 Documentation

No detailed documentation is provided in the original document.

🔧 Technical Details

No technical details are provided in the original document.

📄 License

The model is licensed under cc-by-sa-4.0.

📚 Citation

For more information, please take a look at the original paper.

• Paper: The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
• Authors: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber
• BibTeX:

@article{suzgun2022hupd,
  title={The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications},
  author={Suzgun, Mirac and Melas-Kyriazi, Luke and Sarkar, Suproteem K and Kominers, Scott Duke and Shieber, Stuart M},
  journal={arXiv preprint arXiv:2207.04043},
  year={2022}
}