---
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-ner3
  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-ner3

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: 0.1625
- Precision: 0.7903
- Recall: 0.8291
- F1: 0.8092
- Accuracy: 0.9569

## 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: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 9

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2932        | 0.5   | 4750  | 0.2719          | 0.6321    | 0.7644 | 0.6919 | 0.9309   |
| 0.2262        | 1.0   | 9500  | 0.2197          | 0.7089    | 0.7873 | 0.7460 | 0.9418   |
| 0.2037        | 1.5   | 14250 | 0.2017          | 0.7331    | 0.7982 | 0.7643 | 0.9454   |
| 0.1758        | 2.0   | 19000 | 0.1851          | 0.7634    | 0.8050 | 0.7836 | 0.9499   |
| 0.1699        | 2.5   | 23750 | 0.1910          | 0.7624    | 0.8091 | 0.7850 | 0.9510   |
| 0.1643        | 3.0   | 28500 | 0.1894          | 0.7641    | 0.8125 | 0.7875 | 0.9509   |
| 0.1523        | 3.5   | 33250 | 0.1829          | 0.7574    | 0.8136 | 0.7845 | 0.9502   |
| 0.153         | 4.0   | 38000 | 0.1667          | 0.7794    | 0.8150 | 0.7968 | 0.9544   |
| 0.1445        | 4.5   | 42750 | 0.1745          | 0.7838    | 0.8179 | 0.8005 | 0.9545   |
| 0.1419        | 5.0   | 47500 | 0.1773          | 0.7877    | 0.8195 | 0.8033 | 0.9534   |
| 0.137         | 5.5   | 52250 | 0.1635          | 0.7880    | 0.8211 | 0.8042 | 0.9567   |
| 0.1298        | 6.0   | 57000 | 0.1611          | 0.7837    | 0.8243 | 0.8035 | 0.9560   |
| 0.133         | 6.5   | 61750 | 0.1595          | 0.7908    | 0.8281 | 0.8090 | 0.9564   |
| 0.1264        | 7.0   | 66500 | 0.1640          | 0.7941    | 0.8263 | 0.8099 | 0.9567   |
| 0.1341        | 7.5   | 71250 | 0.1626          | 0.7894    | 0.8286 | 0.8085 | 0.9571   |
| 0.1292        | 8.0   | 76000 | 0.1627          | 0.7902    | 0.8286 | 0.8090 | 0.9569   |
| 0.1291        | 8.5   | 80750 | 0.1620          | 0.7902    | 0.8293 | 0.8093 | 0.9570   |
| 0.1235        | 9.0   | 85500 | 0.1625          | 0.7903    | 0.8291 | 0.8092 | 0.9569   |


### Framework versions

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