---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_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. -->

# distilbert_base_uncased_ledgar

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6496
- Accuracy: 0.8311
- F1 Macro: 0.7116
- F1 Micro: 0.8311

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 3.8165        | 0.11  | 100  | 3.5952          | 0.3489   | 0.0995   | 0.3489   |
| 2.8293        | 0.21  | 200  | 2.6737          | 0.5385   | 0.2375   | 0.5385   |
| 2.2564        | 0.32  | 300  | 2.0960          | 0.6212   | 0.3339   | 0.6212   |
| 1.8259        | 0.43  | 400  | 1.7118          | 0.6792   | 0.4269   | 0.6792   |
| 1.5846        | 0.53  | 500  | 1.4543          | 0.7232   | 0.4987   | 0.7232   |
| 1.3927        | 0.64  | 600  | 1.2635          | 0.758    | 0.5628   | 0.758    |
| 1.2065        | 0.75  | 700  | 1.1217          | 0.7719   | 0.5782   | 0.7719   |
| 1.16          | 0.85  | 800  | 1.0303          | 0.7832   | 0.5984   | 0.7832   |
| 1.0168        | 0.96  | 900  | 0.9443          | 0.7887   | 0.6119   | 0.7887   |
| 0.9006        | 1.07  | 1000 | 0.8958          | 0.7934   | 0.6142   | 0.7934   |
| 0.8956        | 1.17  | 1100 | 0.8517          | 0.8002   | 0.6294   | 0.8002   |
| 0.9159        | 1.28  | 1200 | 0.8184          | 0.8033   | 0.6412   | 0.8033   |
| 0.8237        | 1.39  | 1300 | 0.7814          | 0.8077   | 0.6529   | 0.8077   |
| 0.7341        | 1.49  | 1400 | 0.7654          | 0.8099   | 0.6600   | 0.8099   |
| 0.7475        | 1.6   | 1500 | 0.7458          | 0.8135   | 0.6650   | 0.8135   |
| 0.7699        | 1.71  | 1600 | 0.7288          | 0.8183   | 0.6810   | 0.8183   |
| 0.7472        | 1.81  | 1700 | 0.7125          | 0.8179   | 0.6820   | 0.8179   |
| 0.689         | 1.92  | 1800 | 0.6965          | 0.8201   | 0.6822   | 0.8201   |
| 0.6807        | 2.03  | 1900 | 0.6904          | 0.8192   | 0.6799   | 0.8192   |
| 0.6514        | 2.13  | 2000 | 0.6836          | 0.8239   | 0.6923   | 0.8239   |
| 0.6662        | 2.24  | 2100 | 0.6750          | 0.8267   | 0.7019   | 0.8267   |
| 0.6247        | 2.35  | 2200 | 0.6703          | 0.8284   | 0.7028   | 0.8284   |
| 0.6443        | 2.45  | 2300 | 0.6662          | 0.8265   | 0.7001   | 0.8265   |
| 0.632         | 2.56  | 2400 | 0.6571          | 0.8295   | 0.7078   | 0.8295   |
| 0.5922        | 2.67  | 2500 | 0.6539          | 0.8298   | 0.7084   | 0.8298   |
| 0.6423        | 2.77  | 2600 | 0.6519          | 0.8311   | 0.7139   | 0.8311   |
| 0.6156        | 2.88  | 2700 | 0.6500          | 0.8311   | 0.7123   | 0.8311   |
| 0.6097        | 2.99  | 2800 | 0.6496          | 0.8311   | 0.7116   | 0.8311   |


### Framework versions

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