Instructions to use dtorber/BioNLP-intro-disc-tech-decoder-PLOS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/BioNLP-intro-disc-tech-decoder-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-intro-disc-tech-decoder-PLOS")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/BioNLP-intro-disc-tech-decoder-PLOS") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/BioNLP-intro-disc-tech-decoder-PLOS") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1595c2c751bae49cc6d22797d0180bd85386e711dc99bff6a795e98b708a2eb9
- Size of remote file:
- 648 MB
- SHA256:
- d73bd9ee9809e526e31d229f6365a2e4b780b9de55f3b41d787c39256983b432
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.