Instructions to use dtorber/BioNLP-2024-dtorber-baseline-PLOS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/BioNLP-2024-dtorber-baseline-PLOS 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-2024-dtorber-baseline-PLOS")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/BioNLP-2024-dtorber-baseline-PLOS") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/BioNLP-2024-dtorber-baseline-PLOS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dtorber/BioNLP-2024-dtorber-baseline-PLOS: direct link, hf CLI and curl.
- Browser
- Download file 4.35 kB
-
https://huggingface.co/dtorber/BioNLP-2024-dtorber-baseline-PLOS/resolve/main/training_args.bin
- Command line
-
hf download hf://dtorber/BioNLP-2024-dtorber-baseline-PLOS/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dtorber/BioNLP-2024-dtorber-baseline-PLOS/resolve/main/training_args.bin
4.35 kB
- Xet hash:
- b1f39cde9669aa876e817c14909f56a5d0065b91a4c4a1395a31e7bfd57d2cc9
- Size of remote file:
- 4.35 kB
- SHA256:
- a9ff171a5a7b7dfba221d2f29c7791c7ef6343ae461173fef52f361f22db6744
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