Training complete
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README.md
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metrics:
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- precision
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- recall
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- accuracy
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model-index:
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- name: bert-finetuned-ner
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results: []
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datasets:
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- conll2002
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown 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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- Accuracy: 0.
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## Model description
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## Intended uses & limitations
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall |
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### Framework versions
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- Transformers 4.40.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: bert-finetuned-ner
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1564
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- Precision: 0.75
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- Recall: 0.8045
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- F1: 0.7763
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- Accuracy: 0.9682
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## Model description
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More information needed
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## Intended uses & limitations
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0439 | 1.0 | 1041 | 0.1419 | 0.7464 | 0.7918 | 0.7684 | 0.9674 |
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| 0.0458 | 2.0 | 2082 | 0.1414 | 0.7493 | 0.8028 | 0.7752 | 0.9677 |
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| 0.0261 | 3.0 | 3123 | 0.1564 | 0.75 | 0.8045 | 0.7763 | 0.9682 |
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### Framework versions
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- Transformers 4.40.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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