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")# pip install -U transformers accelerate # 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
Download added_tokens.json from dtorber/BioNLP-conditional-tokens-decoder-eLife: direct link, hf CLI and curl.
- Browser
- Download file 264 Bytes
-
https://huggingface.co/dtorber/BioNLP-conditional-tokens-decoder-eLife/resolve/main/added_tokens.json
- Command line
-
hf download hf://dtorber/BioNLP-conditional-tokens-decoder-eLife/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/dtorber/BioNLP-conditional-tokens-decoder-eLife/resolve/main/added_tokens.json
264 Bytes
| { | |
| "</disc>": 50277, | |
| "</intro>": 50275, | |
| "</lsum>": 50273, | |
| "</tsum>": 50271, | |
| "<disc>": 50276, | |
| "<intro>": 50274, | |
| "<lsum>": 50272, | |
| "<mask_s>": 50266, | |
| "<mask_sg>": 50267, | |
| "<sentence>": 50268, | |
| "<sep>": 50265, | |
| "<text>": 50269, | |
| "<tsum>": 50270 | |
| } | |