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 sparse_weight.pt from annedirkson/BERT_embeddings_ADR_normalization: direct link, hf CLI and curl.
- Browser
- Download file 824 Bytes
-
https://huggingface.co/annedirkson/BERT_embeddings_ADR_normalization/resolve/main/sparse_weight.pt
- Command line
-
hf download hf://annedirkson/BERT_embeddings_ADR_normalization/sparse_weight.pt
-
curl -L -o sparse_weight.pt https://huggingface.co/annedirkson/BERT_embeddings_ADR_normalization/resolve/main/sparse_weight.pt
824 Bytes
- Xet hash:
- 15fad32b9a60f7efa4e950db6d6e3bfb33ad32a262fc8f820e58918319895280
- Size of remote file:
- 824 Bytes
- SHA256:
- b3be7cbe86ece8c01aac1a4935fe710d497d686a09723e55c3c060cc64800142
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