Token Classification
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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
Add evaluation results on conll2003 dataset
#3
by autoevaluator HF Staff - opened
README.md
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datasets:
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- conll2003
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license: mit
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---
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# bert-base-NER
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datasets:
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- conll2003
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license: mit
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model-index:
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- name: dslim/bert-base-NER
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results:
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- task:
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type: token-classification
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name: Token Classification
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dataset:
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name: conll2003
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type: conll2003
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config: conll2003
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split: test
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9118041001560013
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verified: true
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- name: Precision
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type: precision
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value: 0.9211550382257732
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verified: true
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- name: Recall
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type: recall
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value: 0.9306415698281261
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verified: true
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- name: F1
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type: f1
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value: 0.9258740048459675
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verified: true
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- name: loss
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type: loss
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value: 0.48325642943382263
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verified: true
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---
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# bert-base-NER
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