Summarization
Transformers
PyTorch
Safetensors
English
led
text2text-generation
Eval Results (legacy)
Instructions to use AlgorithmicResearchGroup/led_base_16384_arxiv_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlgorithmicResearchGroup/led_base_16384_arxiv_summarization with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="AlgorithmicResearchGroup/led_base_16384_arxiv_summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AlgorithmicResearchGroup/led_base_16384_arxiv_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("AlgorithmicResearchGroup/led_base_16384_arxiv_summarization") - Notebooks
- Google Colab
- Kaggle
Artifact AI commited on
Commit ·
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Parent(s): f486984
add readme
Browse files
README.md
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---
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language: en
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license: apache-2.0
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---
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## Introduction
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A led-base-16384 model to summarize ArXiv papers. Inputs are the abstracts of papers and full documents, and outputs are the summaries of the papers.
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[Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer).
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As described in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-base-16384* was initialized from [*bart-base*](https://huggingface.co/facebook/bart-base) since both models share the exact same architecture. To be able to process 16K tokens, *bart-base*'s position embedding matrix was simply copied 16 times.
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### Rouge
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| Type | Score |
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| --- | --- |
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| `precision` | 0.1839148953011932 |
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| `recall` | 0.14904707945189774 |
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| `fmeasure` | 0.1580026685776864 |
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