---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER-finetuning-Bert-base-prostata
  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. -->

# NER-finetuning-Bert-base-prostata

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0259
- Precision: 0.9818
- Recall: 0.9793
- F1: 0.9806
- Accuracy: 0.9961

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 389  | 0.0267          | 0.9692    | 0.9780 | 0.9736 | 0.9946   |
| 0.0222        | 2.0   | 778  | 0.0291          | 0.9715    | 0.9702 | 0.9709 | 0.9942   |
| 0.0153        | 3.0   | 1167 | 0.0239          | 0.9786    | 0.9767 | 0.9776 | 0.9957   |
| 0.0121        | 4.0   | 1556 | 0.0260          | 0.9761    | 0.9780 | 0.9770 | 0.9952   |
| 0.0121        | 5.0   | 1945 | 0.0264          | 0.9793    | 0.9786 | 0.9790 | 0.9958   |
| 0.0087        | 6.0   | 2334 | 0.0258          | 0.9799    | 0.9773 | 0.9786 | 0.9961   |
| 0.0077        | 7.0   | 2723 | 0.0252          | 0.9831    | 0.9786 | 0.9809 | 0.9964   |
| 0.0053        | 8.0   | 3112 | 0.0259          | 0.9806    | 0.9793 | 0.9799 | 0.9961   |
| 0.005         | 9.0   | 3501 | 0.0261          | 0.9806    | 0.9793 | 0.9799 | 0.9961   |
| 0.005         | 10.0  | 3890 | 0.0259          | 0.9818    | 0.9793 | 0.9806 | 0.9961   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1