Token Classification
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
English
bert
Eval Results (legacy)
Instructions to use dslim/bert-base-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dslim/bert-base-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dslim/bert-base-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#8)
- Adding `safetensors` variant of this model (f128abadec23cd734101cf1caaaa2e31c82bb626) Co-authored-by: Nicolas Patry <Narsil@users.noreply.huggingface.co>7a1d333
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allow flax
688d910
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initial commit
7dcd7cf