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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2dad94c08018a25cd720a9842fed536d3f96305fafaaad88553bf6382d0bb8f2
- Size of remote file:
- 4.41 kB
- SHA256:
- 7c19601dbab3357efa815c80489a9a2d1bf4d495f2814951921a99babd74b759
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.