---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_base_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. -->

# roberta_base_ledgar

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6144
- Accuracy: 0.8449
- F1 Macro: 0.7396
- F1 Micro: 0.8449

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 3.1205        | 0.11  | 100  | 2.7420          | 0.5459   | 0.2772   | 0.5459   |
| 2.0491        | 0.21  | 200  | 1.8506          | 0.6623   | 0.3967   | 0.6623   |
| 1.6304        | 0.32  | 300  | 1.4552          | 0.722    | 0.4953   | 0.722    |
| 1.3418        | 0.43  | 400  | 1.2101          | 0.7574   | 0.5559   | 0.7574   |
| 1.2156        | 0.53  | 500  | 1.0701          | 0.7728   | 0.5868   | 0.7728   |
| 1.0994        | 0.64  | 600  | 0.9578          | 0.7922   | 0.6223   | 0.7922   |
| 0.9857        | 0.75  | 700  | 0.8957          | 0.7968   | 0.6274   | 0.7968   |
| 0.9507        | 0.85  | 800  | 0.8474          | 0.7999   | 0.6368   | 0.7999   |
| 0.8734        | 0.96  | 900  | 0.7990          | 0.814    | 0.6675   | 0.814    |
| 0.7802        | 1.07  | 1000 | 0.7788          | 0.8128   | 0.6606   | 0.8128   |
| 0.7869        | 1.17  | 1100 | 0.7537          | 0.8178   | 0.6742   | 0.8178   |
| 0.8341        | 1.28  | 1200 | 0.7309          | 0.8232   | 0.6881   | 0.8232   |
| 0.7372        | 1.39  | 1300 | 0.7157          | 0.8219   | 0.6876   | 0.8219   |
| 0.661         | 1.49  | 1400 | 0.7058          | 0.8224   | 0.6941   | 0.8224   |
| 0.6932        | 1.6   | 1500 | 0.6944          | 0.8258   | 0.6981   | 0.8258   |
| 0.7305        | 1.71  | 1600 | 0.6807          | 0.8292   | 0.7058   | 0.8292   |
| 0.6952        | 1.81  | 1700 | 0.6627          | 0.8291   | 0.7066   | 0.8291   |
| 0.6583        | 1.92  | 1800 | 0.6509          | 0.8322   | 0.7086   | 0.8322   |
| 0.6157        | 2.03  | 1900 | 0.6487          | 0.8321   | 0.7120   | 0.8321   |
| 0.5817        | 2.13  | 2000 | 0.6429          | 0.8347   | 0.7164   | 0.8347   |
| 0.6002        | 2.24  | 2100 | 0.6375          | 0.836    | 0.7202   | 0.836    |
| 0.5786        | 2.35  | 2200 | 0.6344          | 0.8401   | 0.7318   | 0.8401   |
| 0.595         | 2.45  | 2300 | 0.6276          | 0.8382   | 0.7221   | 0.8382   |
| 0.564         | 2.56  | 2400 | 0.6197          | 0.8416   | 0.7353   | 0.8416   |
| 0.5404        | 2.67  | 2500 | 0.6157          | 0.8438   | 0.7412   | 0.8438   |
| 0.5706        | 2.77  | 2600 | 0.6162          | 0.8418   | 0.7368   | 0.8418   |
| 0.5419        | 2.88  | 2700 | 0.6148          | 0.844    | 0.7383   | 0.844    |
| 0.5631        | 2.99  | 2800 | 0.6144          | 0.8449   | 0.7396   | 0.8449   |


### Framework versions

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