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
Download tokenizer_config.json from dslim/bert-base-NER: direct link, hf CLI and curl.
- Browser
- Download file 59 Bytes
-
https://huggingface.co/dslim/bert-base-NER/resolve/refs%2Fpr%2F27/tokenizer_config.json
- Command line
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hf download hf://dslim/bert-base-NER@refs/pr/27/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/dslim/bert-base-NER/resolve/refs%2Fpr%2F27/tokenizer_config.json
59 Bytes
| {"do_lower_case": false, "max_len": 512, "init_inputs": []} |