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") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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by vpkprasanna - opened
README.md
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@@ -48,8 +48,8 @@ The training dataset distinguishes between the beginning and continuation of an
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Abbreviation|Description
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O|Outside of a named entity
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B-
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I-
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B-PER |Beginning of a person’s name right after another person’s name
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I-PER |Person’s name
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B-ORG |Beginning of an organization right after another organization
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Abbreviation|Description
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O|Outside of a named entity
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B-MISC |Beginning of a miscellaneous entity right after another miscellaneous entity
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I-MISC | Miscellaneous entity
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B-PER |Beginning of a person’s name right after another person’s name
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I-PER |Person’s name
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B-ORG |Beginning of an organization right after another organization
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