Instructions to use alinet/pubmed-bart-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alinet/pubmed-bart-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="alinet/pubmed-bart-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alinet/pubmed-bart-base") model = AutoModelForSeq2SeqLM.from_pretrained("alinet/pubmed-bart-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from alinet/pubmed-bart-base: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/alinet/pubmed-bart-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://alinet/pubmed-bart-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/alinet/pubmed-bart-base/resolve/main/flax_model.msgpack
558 MB
- Xet hash:
- e42b8521f8619fd762f2c0a52cce42372cf527d9e6ea69ac34bb80b08ec50d73
- Size of remote file:
- 558 MB
- SHA256:
- 65335ecb9bc5381d5ef4a6c9c6aa92bcb6a758e4443ca88d92976a669204c2fa
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