---
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-ner2
  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-ner2

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: 0.1445
- Precision: 0.8221
- Recall: 0.8509
- F1: 0.8362
- Accuracy: 0.9656

## 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 OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1275        | 1.0   | 4750  | 0.1217          | 0.7861    | 0.8216 | 0.8034 | 0.9602   |
| 0.0985        | 2.0   | 9500  | 0.1209          | 0.8166    | 0.8266 | 0.8215 | 0.9630   |
| 0.0716        | 3.0   | 14250 | 0.1175          | 0.8209    | 0.8493 | 0.8349 | 0.9652   |
| 0.0448        | 4.0   | 19000 | 0.1360          | 0.8166    | 0.8470 | 0.8315 | 0.9652   |
| 0.037         | 5.0   | 23750 | 0.1445          | 0.8221    | 0.8509 | 0.8362 | 0.9656   |


### Framework versions

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