Bart Finetuned Keyphrase Extraction

🚀 BART微調用於關鍵詞生成

本模型是針對關鍵詞生成任務微調的 bart-base 模型（Lewis 等人，2019），微調基於以下語料庫的片段完成（Glazkova & Morozov，2023）：

• Krapivin（Krapivin 等人，2009）
• Inspec（Hulth，2003）
• KPTimes（Gallina，2019）
• DUC - 2001（Wan，2008）
• PubMed（Schutz，2008）
• NamedKeys（Gero & Ho，2019）

🚀 快速開始

以下是使用該模型進行關鍵詞生成的示例代碼：

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("aglazkova/bart_finetuned_keyphrase_extraction")
model = AutoModelForSeq2SeqLM.from_pretrained("aglazkova/bart_finetuned_keyphrase_extraction")

text = "In this paper, we investigate cross-domain limitations of keyphrase generation using the models for abstractive text summarization.\
        We present an evaluation of BART fine-tuned for keyphrase generation across three types of texts, \
        namely scientific texts from computer science and biomedical domains and news texts. \
        We explore the role of transfer learning between different domains to improve the model performance on small text corpora."

tokenized_text = tokenizer.prepare_seq2seq_batch([text], return_tensors='pt')
translation = model.generate(**tokenized_text)
translated_text = tokenizer.batch_decode(translation, skip_special_tokens=True)[0]
print(translated_text)

📚 詳細文檔

訓練超參數

訓練過程中使用了以下超參數：

BibTeX引用

@InProceedings{10.1007/978-3-031-67826-4_19,
author="Glazkova, Anna
and Morozov, Dmitry",
title="Cross-Domain Robustness of Transformer-Based Keyphrase Generation",
booktitle="Data Analytics and Management in Data Intensive Domains",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="249--265"
}