Hupd T5 Small

🚀 HUPD T5-Small要約モデル

このHUPD T5-Small要約モデルは、HUPDデータセットでファインチューニングされました。元々はこの論文で紹介されました。

ハーバードUSPTO特許データセットに関する詳細情報については、プロジェクトのウェブサイトまたはプロジェクトのGitHubリポジトリをご覧ください。

🚀 クイックスタート

使い方

このモデルは、マスク言語モデリングのパイプラインで直接使用することができます。

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)

以下は出力結果です。

[{'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.'}]

または、以下のようにモデルをロードして使用することもできます。

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)

📄 ライセンス

このモデルは、CC BY-SA 4.0ライセンスの下で提供されています。

📚 詳細ドキュメント

引用情報

詳細については、元の論文を参照してください。

• 論文: The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
• 著者: 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}
}