Instructions to use dtorber/BioNLP-conditional-tokens-decoder-eLife with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/BioNLP-conditional-tokens-decoder-eLife 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="dtorber/BioNLP-conditional-tokens-decoder-eLife")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/BioNLP-conditional-tokens-decoder-eLife") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/BioNLP-conditional-tokens-decoder-eLife") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 609d713261ef9774356288f038fcb416acce4d47e28b011e36b733222566319b
- Size of remote file:
- 648 MB
- SHA256:
- 7e20e2cb445453ed69b56e7bcf61c4bc2bf7e1e6487e5857b11d501fdcaa92d8
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