Instructions to use batterydata/bert-abbrev-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use batterydata/bert-abbrev-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="batterydata/bert-abbrev-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("batterydata/bert-abbrev-cased") model = AutoModelForTokenClassification.from_pretrained("batterydata/bert-abbrev-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from batterydata/bert-abbrev-cased: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/batterydata/bert-abbrev-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://batterydata/bert-abbrev-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/batterydata/bert-abbrev-cased/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 58895af6a9f8e6f2ad326a71b1c3eaafaf27838eb7ead6bfc4aa4f1ba9e6955a
- Size of remote file:
- 431 MB
- SHA256:
- 350dde085ff13a860350a89a180bcf7dd07b4befa2815f1b88b0afd0bb18f616
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.