nsi319/Finetune-Transformers
Abstractive text summarization by fine-tuning seq2seq models.
This helps developers fine-tune large language models for abstractive text summarization. It takes a pre-trained sequence-to-sequence model and your domain-specific text data, then outputs a specialized model that can summarize text more accurately for your particular use case. This tool is for machine learning engineers and data scientists who need to adapt generic summarization models to specific datasets, like news articles or research papers.
No commits in the last 6 months.
Use this if you are a machine learning engineer or data scientist who needs to improve the accuracy of text summarization for a specific type of content by adapting an existing model.
Not ideal if you are looking for a ready-to-use summarization tool without any coding or model training.
Stars
39
Forks
10
Language
Python
License
—
Category
Last pushed
Feb 18, 2021
Commits (30d)
0
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