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")# 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 flax_model.msgpack from dslim/bert-base-NER: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/dslim/bert-base-NER/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://dslim/bert-base-NER/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/dslim/bert-base-NER/resolve/main/flax_model.msgpack
431 MB
- Xet hash:
- 2964ccb88b1124ba97deea72180c413c68144139ca99ef739f27a75c73214321
- Size of remote file:
- 431 MB
- SHA256:
- a124466eab9adb43377d35d32afe77313fceeb16b74b106f3742884c666a2c1e
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