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---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3391
- Precision: 0.8826
- Recall: 0.9138
- F1: 0.8979
- Accuracy: 0.9518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0318 | 1.0 | 680 | 0.4800 | 0.8075 | 0.8632 | 0.8344 | 0.9183 |
| 0.0206 | 2.0 | 1360 | 0.4822 | 0.8332 | 0.8634 | 0.8480 | 0.9233 |
| 0.0116 | 3.0 | 2040 | 0.5227 | 0.8167 | 0.8683 | 0.8417 | 0.9211 |
| 0.0093 | 4.0 | 2720 | 0.5366 | 0.8230 | 0.8749 | 0.8482 | 0.9246 |
| 0.0077 | 5.0 | 3400 | 0.5384 | 0.8370 | 0.8688 | 0.8526 | 0.9249 |
| 0.0061 | 6.0 | 4080 | 0.5450 | 0.8418 | 0.8754 | 0.8583 | 0.9275 |
| 0.0048 | 7.0 | 4760 | 0.5570 | 0.8346 | 0.8765 | 0.8550 | 0.9262 |
| 0.0084 | 8.0 | 5440 | 0.5565 | 0.8353 | 0.8765 | 0.8554 | 0.9261 |
| 0.0073 | 9.0 | 6120 | 0.5693 | 0.8353 | 0.8751 | 0.8547 | 0.9261 |
| 0.0058 | 10.0 | 6800 | 0.5688 | 0.8361 | 0.8766 | 0.8559 | 0.9265 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.4.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
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