Instructions to use batterydata/bert-abbrev-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use batterydata/bert-abbrev-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="batterydata/bert-abbrev-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("batterydata/bert-abbrev-uncased") model = AutoModelForTokenClassification.from_pretrained("batterydata/bert-abbrev-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4372a26417dc5691ab6a5338b57497752974c648bc1882edd632dec4e23e07d5
- Size of remote file:
- 436 MB
- SHA256:
- a1d70388692c55d5309d6d7aa92a4477ed4da43797b8f372c1b37b223b63e373
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