Instructions to use rcd12/bert-conll2003-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rcd12/bert-conll2003-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rcd12/bert-conll2003-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rcd12/bert-conll2003-ner") model = AutoModelForTokenClassification.from_pretrained("rcd12/bert-conll2003-ner") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1125
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- Precision: 0.9051
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- Recall: 0.9192
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- F1: 0.9121
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- Accuracy: 0.9827
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0473 | 1.0 | 878 | 0.0407 | 0.9256 | 0.9390 | 0.9323 | 0.9891 |
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| 0.0199 | 2.0 | 1756 | 0.0386 | 0.9433 | 0.9481 | 0.9457 | 0.9909 |
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| 0.0126 | 3.0 | 2634 | 0.0367 | 0.9444 | 0.9527 | 0.9485 | 0.9914 |
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### Framework versions
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