Token Classification
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
English
bert
Eval Results (legacy)
Instructions to use dslim/bert-large-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dslim/bert-large-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dslim/bert-large-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/bert-large-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-large-NER") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dca820624103bac3349ce147f84a7c3f0f545fbbacad37cb5d05cef1c8af5d3c
- Size of remote file:
- 1.33 GB
- SHA256:
- db4d615a10a12cfe134388d43d8fba48e909e7f7afa3e4d3b1bd2c24ff7cea3d
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