---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-ner-final
  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-cased-finetuned-ner-final

This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7343
- Precision: 0.8366
- Recall: 0.8508
- F1: 0.8436
- Accuracy: 0.9652

## 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: 1.58775582613963e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.115325565287072
- num_epochs: 8
- label_smoothing_factor: 0.114373096835144

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7514        | 1.0   | 4250  | 0.7540          | 0.8011    | 0.8113 | 0.8062 | 0.9580   |
| 0.7317        | 2.0   | 8500  | 0.7358          | 0.8277    | 0.8302 | 0.8289 | 0.9619   |
| 0.7212        | 3.0   | 12750 | 0.7329          | 0.8183    | 0.8442 | 0.8310 | 0.9635   |
| 0.7023        | 4.0   | 17000 | 0.7346          | 0.8192    | 0.8459 | 0.8324 | 0.9640   |
| 0.6935        | 5.0   | 21250 | 0.7343          | 0.8366    | 0.8508 | 0.8436 | 0.9652   |
| 0.6851        | 6.0   | 25500 | 0.7409          | 0.8319    | 0.8514 | 0.8415 | 0.9646   |
| 0.678         | 7.0   | 29750 | 0.7450          | 0.8299    | 0.8528 | 0.8412 | 0.9645   |
| 0.672         | 8.0   | 34000 | 0.7475          | 0.8349    | 0.8525 | 0.8436 | 0.9646   |


### Framework versions

- Transformers 4.50.1
- Pytorch 2.5.1+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1