Instructions to use dtorber/BioNLP-tech-decoder-eLife with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/BioNLP-tech-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-tech-decoder-eLife")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/BioNLP-tech-decoder-eLife") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/BioNLP-tech-decoder-eLife", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_config.json from dtorber/BioNLP-tech-decoder-eLife: direct link, hf CLI and curl.
- Browser
- Download file 163 Bytes
-
https://huggingface.co/dtorber/BioNLP-tech-decoder-eLife/resolve/main/generation_config.json
- Command line
-
hf download hf://dtorber/BioNLP-tech-decoder-eLife/generation_config.json
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curl -L -o generation_config.json https://huggingface.co/dtorber/BioNLP-tech-decoder-eLife/resolve/main/generation_config.json
163 Bytes
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 0, | |
| "decoder_start_token_id": 2, | |
| "eos_token_id": 2, | |
| "pad_token_id": 1, | |
| "transformers_version": "4.35.2" | |
| } | |