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---
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license: apache-2.0
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base_model: bert-base-cased
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tags:
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- generated_from_trainer
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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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should probably proofread and complete it, then remove this comment. -->
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# bert-finetuned-ner
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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: 0.3391
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- Precision: 0.8826
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- Recall: 0.9138
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- F1: 0.8979
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- Accuracy: 0.9518
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 10
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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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| 0.0318 | 1.0 | 680 | 0.4800 | 0.8075 | 0.8632 | 0.8344 | 0.9183 |
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| 0.0206 | 2.0 | 1360 | 0.4822 | 0.8332 | 0.8634 | 0.8480 | 0.9233 |
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| 0.0116 | 3.0 | 2040 | 0.5227 | 0.8167 | 0.8683 | 0.8417 | 0.9211 |
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| 0.0093 | 4.0 | 2720 | 0.5366 | 0.8230 | 0.8749 | 0.8482 | 0.9246 |
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| 0.0077 | 5.0 | 3400 | 0.5384 | 0.8370 | 0.8688 | 0.8526 | 0.9249 |
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| 0.0061 | 6.0 | 4080 | 0.5450 | 0.8418 | 0.8754 | 0.8583 | 0.9275 |
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| 0.0048 | 7.0 | 4760 | 0.5570 | 0.8346 | 0.8765 | 0.8550 | 0.9262 |
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| 0.0084 | 8.0 | 5440 | 0.5565 | 0.8353 | 0.8765 | 0.8554 | 0.9261 |
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| 0.0073 | 9.0 | 6120 | 0.5693 | 0.8353 | 0.8751 | 0.8547 | 0.9261 |
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| 0.0058 | 10.0 | 6800 | 0.5688 | 0.8361 | 0.8766 | 0.8559 | 0.9265 |
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### Framework versions
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- Transformers 4.43.3
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- Pytorch 2.4.0+cu118
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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