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 pytorch_model.bin from dslim/bert-base-NER: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/dslim/bert-base-NER/resolve/4504313a58019ae2535de35aa14f4f3f47a4ac00/pytorch_model.bin
- Command line
-
hf download hf://dslim/bert-base-NER@4504313a58019ae2535de35aa14f4f3f47a4ac00/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dslim/bert-base-NER/resolve/4504313a58019ae2535de35aa14f4f3f47a4ac00/pytorch_model.bin
433 MB
- Xet hash:
- a07b031f971c05d49a77781f20d5f7d6e758bba7fa63ab88715a230f49467088
- Size of remote file:
- 433 MB
- SHA256:
- 4c0b01790e435da1337ea519d76e747427f2d3ee9c0e49b4952caa06298021f6
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