Instructions to use microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract") model = AutoModelForMaskedLM.from_pretrained("microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download config.json from microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract: direct link, hf CLI and curl.
- Browser
- Download file 337 Bytes
-
https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract/resolve/29f6836d2342f330d2eff0a7be7c06bf04dd5a49/config.json
- Command line
-
hf download hf://microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract@29f6836d2342f330d2eff0a7be7c06bf04dd5a49/config.json
-
curl -L -o config.json https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract/resolve/29f6836d2342f330d2eff0a7be7c06bf04dd5a49/config.json
337 Bytes
| { | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "type_vocab_size": 2, | |
| "vocab_size": 30522 | |
| } | |