Instructions to use dtorber/BioNLP-conditional-tokens-encoder-eLife with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/BioNLP-conditional-tokens-encoder-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-encoder-eLife")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/BioNLP-conditional-tokens-encoder-eLife") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/BioNLP-conditional-tokens-encoder-eLife") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3885ff1f1d53924b0937877500f92e13c202fbddfab46617a8e8251e1ab4cc2
- Size of remote file:
- 648 MB
- SHA256:
- 4fcbdd29f35f501333c31720f9804b4d0c4d36120119147ecb158e159ae0986e
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