Token Classification
Transformers
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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") - Inference
- Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
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by Aamer - opened
- tokenizer_config.json +1 -1
tokenizer_config.json
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{"do_lower_case": false, "max_len": 512, "init_inputs": []}
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{"do_lower_case": false, "max_len": 512, "init_inputs": [], "label_all_tokens" = false}
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