---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_base_uncased_ledgar
  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_uncased_ledgar

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6676
- Accuracy: 0.8349
- F1 Macro: 0.7127
- F1 Micro: 0.8349

## 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: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 3.6919        | 0.11  | 100  | 3.4439          | 0.4049   | 0.1512   | 0.4049   |
| 2.7312        | 0.21  | 200  | 2.5762          | 0.5766   | 0.3025   | 0.5766   |
| 2.1872        | 0.32  | 300  | 2.0346          | 0.656    | 0.3994   | 0.656    |
| 1.7869        | 0.43  | 400  | 1.6759          | 0.7075   | 0.4796   | 0.7075   |
| 1.5593        | 0.53  | 500  | 1.4354          | 0.7454   | 0.5447   | 0.7454   |
| 1.388         | 0.64  | 600  | 1.2759          | 0.7695   | 0.5778   | 0.7695   |
| 1.214         | 0.75  | 700  | 1.1428          | 0.7806   | 0.5891   | 0.7806   |
| 1.158         | 0.85  | 800  | 1.0531          | 0.784    | 0.5955   | 0.784    |
| 1.0284        | 0.96  | 900  | 0.9726          | 0.7944   | 0.6182   | 0.7944   |
| 0.9249        | 1.07  | 1000 | 0.9276          | 0.8009   | 0.6295   | 0.8009   |
| 0.9046        | 1.17  | 1100 | 0.8824          | 0.8058   | 0.6413   | 0.8058   |
| 0.9312        | 1.28  | 1200 | 0.8425          | 0.8081   | 0.6450   | 0.8081   |
| 0.8329        | 1.39  | 1300 | 0.8096          | 0.8135   | 0.6585   | 0.8135   |
| 0.7601        | 1.49  | 1400 | 0.7946          | 0.8148   | 0.6646   | 0.8148   |
| 0.7812        | 1.6   | 1500 | 0.7766          | 0.8192   | 0.6739   | 0.8192   |
| 0.7944        | 1.71  | 1600 | 0.7585          | 0.8221   | 0.6800   | 0.8221   |
| 0.7632        | 1.81  | 1700 | 0.7363          | 0.8269   | 0.6902   | 0.8269   |
| 0.7027        | 1.92  | 1800 | 0.7229          | 0.8227   | 0.6793   | 0.8227   |
| 0.671         | 2.03  | 1900 | 0.7145          | 0.8263   | 0.6870   | 0.8263   |
| 0.6361        | 2.13  | 2000 | 0.7067          | 0.8277   | 0.6952   | 0.8277   |
| 0.6615        | 2.24  | 2100 | 0.6969          | 0.8281   | 0.6974   | 0.8281   |
| 0.6432        | 2.35  | 2200 | 0.6908          | 0.8311   | 0.7054   | 0.8311   |
| 0.648         | 2.45  | 2300 | 0.6850          | 0.8304   | 0.7011   | 0.8304   |
| 0.631         | 2.56  | 2400 | 0.6750          | 0.8323   | 0.7063   | 0.8323   |
| 0.575         | 2.67  | 2500 | 0.6718          | 0.8337   | 0.7094   | 0.8337   |
| 0.6444        | 2.77  | 2600 | 0.6701          | 0.8332   | 0.7102   | 0.8332   |
| 0.6054        | 2.88  | 2700 | 0.6690          | 0.8346   | 0.7122   | 0.8346   |
| 0.6123        | 2.99  | 2800 | 0.6676          | 0.8349   | 0.7127   | 0.8349   |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2