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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b6152e77b2a21764ca82aaaf390d2b7f203332d9fed63c1baf2e48764bd4307
- Size of remote file:
- 1.29 GB
- SHA256:
- 2d2dc40a9d77c9612210214a18d5b4d7a1bd53f436e2639d4dc184f8b973fe5a
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