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 onnx/added_tokens.json from dslim/bert-base-NER: direct link, hf CLI and curl.
- Browser
- Download file 82 Bytes
-
https://huggingface.co/dslim/bert-base-NER/resolve/main/onnx/added_tokens.json
- Command line
-
hf download hf://dslim/bert-base-NER/onnx/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/dslim/bert-base-NER/resolve/main/onnx/added_tokens.json
82 Bytes
| { | |
| "[CLS]": 101, | |
| "[MASK]": 103, | |
| "[PAD]": 0, | |
| "[SEP]": 102, | |
| "[UNK]": 100 | |
| } | |