Instructions to use annedirkson/BERT_embeddings_ADR_normalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use annedirkson/BERT_embeddings_ADR_normalization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="annedirkson/BERT_embeddings_ADR_normalization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("annedirkson/BERT_embeddings_ADR_normalization") model = AutoModel.from_pretrained("annedirkson/BERT_embeddings_ADR_normalization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from annedirkson/BERT_embeddings_ADR_normalization: direct link, hf CLI and curl.
- Browser
- Download file 434 MB
-
https://huggingface.co/annedirkson/BERT_embeddings_ADR_normalization/resolve/main/tf_model.h5
- Command line
-
hf download hf://annedirkson/BERT_embeddings_ADR_normalization/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/annedirkson/BERT_embeddings_ADR_normalization/resolve/main/tf_model.h5
434 MB
- Xet hash:
- c984ccb61d9539d26b47c18741fe87d9bd57507cfedf0b2c1b8d103d2b3790cf
- Size of remote file:
- 434 MB
- SHA256:
- 1e161c4daef5fd00ef17abccec00ea69b0f1f36967819fe543167fbe08202eb5
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