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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-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-base-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3910
- Precision: 0.9616
- Recall: 0.9637
- F1: 0.9627
- Accuracy: 0.9560
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3052 | 1.0 | 3334 | 0.2630 | 0.9365 | 0.9367 | 0.9366 | 0.9228 |
| 0.2104 | 2.0 | 6668 | 0.2481 | 0.9418 | 0.9537 | 0.9477 | 0.9400 |
| 0.163 | 3.0 | 10002 | 0.2390 | 0.9495 | 0.9606 | 0.9550 | 0.9479 |
| 0.1151 | 4.0 | 13336 | 0.2516 | 0.9549 | 0.9616 | 0.9583 | 0.9515 |
| 0.0809 | 5.0 | 16670 | 0.2887 | 0.9590 | 0.9556 | 0.9573 | 0.9493 |
| 0.0625 | 6.0 | 20004 | 0.2912 | 0.9573 | 0.9611 | 0.9592 | 0.9520 |
| 0.0516 | 7.0 | 23338 | 0.3139 | 0.9581 | 0.9563 | 0.9572 | 0.9501 |
| 0.0388 | 8.0 | 26672 | 0.3070 | 0.9605 | 0.9600 | 0.9602 | 0.9531 |
| 0.0273 | 9.0 | 30006 | 0.3344 | 0.9607 | 0.9617 | 0.9612 | 0.9535 |
| 0.0252 | 10.0 | 33340 | 0.3547 | 0.9608 | 0.9638 | 0.9623 | 0.9554 |
| 0.0242 | 11.0 | 36674 | 0.3726 | 0.9600 | 0.9619 | 0.9610 | 0.9541 |
| 0.0119 | 12.0 | 40008 | 0.3727 | 0.9602 | 0.9623 | 0.9612 | 0.9546 |
| 0.0078 | 13.0 | 43342 | 0.3772 | 0.9617 | 0.9639 | 0.9628 | 0.9562 |
| 0.0078 | 14.0 | 46676 | 0.3904 | 0.9615 | 0.9638 | 0.9627 | 0.9560 |
| 0.0026 | 15.0 | 50010 | 0.3910 | 0.9616 | 0.9637 | 0.9627 | 0.9560 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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