---
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
  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

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: 0.2415
- Precision: 0.8271
- Recall: 0.8524
- F1: 0.8396
- Accuracy: 0.9644

## 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: 4
- eval_batch_size: 4
- 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
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1239        | 1.0   | 9500  | 0.1210          | 0.8028    | 0.8243 | 0.8134 | 0.9614   |
| 0.0939        | 2.0   | 19000 | 0.1206          | 0.8218    | 0.8313 | 0.8265 | 0.9638   |
| 0.0737        | 3.0   | 28500 | 0.1306          | 0.8201    | 0.8447 | 0.8323 | 0.9642   |
| 0.0483        | 4.0   | 38000 | 0.1526          | 0.8239    | 0.8477 | 0.8356 | 0.9647   |
| 0.0301        | 5.0   | 47500 | 0.1939          | 0.8354    | 0.8529 | 0.8441 | 0.9649   |
| 0.0157        | 6.0   | 57000 | 0.2213          | 0.8310    | 0.8549 | 0.8428 | 0.9647   |
| 0.0099        | 7.0   | 66500 | 0.2415          | 0.8271    | 0.8524 | 0.8396 | 0.9644   |


### Framework versions

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