Instructions to use Mia2024/CS5100TextSummarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mia2024/CS5100TextSummarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Mia2024/CS5100TextSummarization") model = AutoModelForSeq2SeqLM.from_pretrained("Mia2024/CS5100TextSummarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Mia2024/CS5100TextSummarization: direct link, hf CLI and curl.
- Browser
- Download file 694 Bytes
-
https://huggingface.co/Mia2024/CS5100TextSummarization/resolve/main/README.md
- Command line
-
hf download hf://Mia2024/CS5100TextSummarization/README.md
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curl -L -o README.md https://huggingface.co/Mia2024/CS5100TextSummarization/resolve/main/README.md
694 Bytes
metadata
library_name: transformers
license: mit
datasets:
- knkarthick/dialogsum
language:
- en
metrics:
- rouge
Model Card for Model ID
Model Details
Model Description
Final Project for 2024 Fall CS5100 Foundations of Artificial Intelligence at Roux Institute, Northeastern University.
- Developed by: Nan Chen
- Language(s) (NLP): Python, Transformers
- Finetuned from model: facebook/bart-large-cnn
Model Sources [optional]
- Repository: Final folder in Github CS6120NLP
- Demo: Space link.