---
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-ner5
  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-ner5

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: 0.6873
- Precision: 0.8196
- Recall: 0.8344
- F1: 0.8269
- Accuracy: 0.9611

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.726         | 1.0   | 1188 | 0.7145          | 0.7742    | 0.7897 | 0.7819 | 0.9525   |
| 0.6889        | 2.0   | 2376 | 0.6936          | 0.8085    | 0.8085 | 0.8085 | 0.9573   |
| 0.6676        | 3.0   | 3564 | 0.6818          | 0.8023    | 0.8239 | 0.8129 | 0.9584   |
| 0.6569        | 4.0   | 4752 | 0.6792          | 0.8154    | 0.8293 | 0.8223 | 0.9610   |
| 0.6452        | 5.0   | 5940 | 0.6883          | 0.8182    | 0.8254 | 0.8218 | 0.9600   |
| 0.6371        | 6.0   | 7128 | 0.6876          | 0.8237    | 0.8336 | 0.8286 | 0.9615   |
| 0.6342        | 7.0   | 8316 | 0.6863          | 0.8194    | 0.8370 | 0.8281 | 0.9615   |
| 0.6298        | 8.0   | 9504 | 0.6873          | 0.8196    | 0.8344 | 0.8269 | 0.9611   |


### Framework versions

- Transformers 4.50.1
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1