---
license: apache-2.0
base_model: projecte-aina/roberta-base-ca-v2-cased-te
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: AUTH_300524_epoch_4
  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. -->

# AUTH_300524_epoch_4

This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4656
- Accuracy: 0.9038
- Precision: 0.9047
- Recall: 0.9038
- F1: 0.9038
- Ratio: 0.4760

## 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: 16
- eval_batch_size: 16
- seed: 47
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- lr_scheduler_warmup_steps: 4
- num_epochs: 1
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Ratio  |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.4294        | 0.0354 | 10   | 0.5003          | 0.9018   | 0.9020    | 0.9018 | 0.9018 | 0.5100 |
| 0.386         | 0.0708 | 20   | 0.5308          | 0.8938   | 0.8952    | 0.8938 | 0.8937 | 0.4699 |
| 0.4424        | 0.1062 | 30   | 0.4881          | 0.8998   | 0.9000    | 0.8998 | 0.8998 | 0.4900 |
| 0.42          | 0.1416 | 40   | 0.4916          | 0.9068   | 0.9091    | 0.9068 | 0.9067 | 0.4629 |
| 0.418         | 0.1770 | 50   | 0.4905          | 0.8968   | 0.8968    | 0.8968 | 0.8968 | 0.4950 |
| 0.4402        | 0.2124 | 60   | 0.5034          | 0.8988   | 0.9027    | 0.8988 | 0.8986 | 0.4509 |
| 0.4141        | 0.2478 | 70   | 0.5085          | 0.9028   | 0.9061    | 0.9028 | 0.9026 | 0.4549 |
| 0.4836        | 0.2832 | 80   | 0.4875          | 0.9028   | 0.9029    | 0.9028 | 0.9028 | 0.4910 |
| 0.4361        | 0.3186 | 90   | 0.4876          | 0.8998   | 0.8998    | 0.8998 | 0.8998 | 0.4980 |
| 0.45          | 0.3540 | 100  | 0.4985          | 0.8938   | 0.8938    | 0.8938 | 0.8938 | 0.5040 |
| 0.4648        | 0.3894 | 110  | 0.5236          | 0.8858   | 0.8954    | 0.8858 | 0.8851 | 0.4218 |
| 0.4714        | 0.4248 | 120  | 0.5009          | 0.8888   | 0.8888    | 0.8888 | 0.8888 | 0.5010 |
| 0.4628        | 0.4602 | 130  | 0.4971          | 0.8868   | 0.8871    | 0.8868 | 0.8867 | 0.4850 |
| 0.4513        | 0.4956 | 140  | 0.4971          | 0.8968   | 0.9003    | 0.8968 | 0.8966 | 0.4529 |
| 0.4905        | 0.5310 | 150  | 0.4873          | 0.8938   | 0.8969    | 0.8938 | 0.8936 | 0.4559 |
| 0.4875        | 0.5664 | 160  | 0.4760          | 0.8948   | 0.8948    | 0.8948 | 0.8948 | 0.4950 |
| 0.4593        | 0.6018 | 170  | 0.4818          | 0.8918   | 0.8918    | 0.8918 | 0.8918 | 0.4960 |
| 0.403         | 0.6372 | 180  | 0.4927          | 0.8928   | 0.8936    | 0.8928 | 0.8927 | 0.4770 |
| 0.4838        | 0.6726 | 190  | 0.5039          | 0.8958   | 0.9001    | 0.8958 | 0.8955 | 0.4479 |
| 0.4512        | 0.7080 | 200  | 0.4913          | 0.8978   | 0.9009    | 0.8978 | 0.8976 | 0.4559 |
| 0.4415        | 0.7434 | 210  | 0.4874          | 0.8988   | 0.8989    | 0.8988 | 0.8988 | 0.4930 |
| 0.5317        | 0.7788 | 220  | 0.4786          | 0.9018   | 0.9021    | 0.9018 | 0.9018 | 0.4860 |
| 0.4718        | 0.8142 | 230  | 0.4746          | 0.9008   | 0.9041    | 0.9008 | 0.9006 | 0.4549 |
| 0.473         | 0.8496 | 240  | 0.4686          | 0.9028   | 0.9044    | 0.9028 | 0.9027 | 0.4689 |
| 0.499         | 0.8850 | 250  | 0.4689          | 0.9028   | 0.9031    | 0.9028 | 0.9028 | 0.4870 |
| 0.5655        | 0.9204 | 260  | 0.4661          | 0.9068   | 0.9074    | 0.9068 | 0.9068 | 0.4810 |
| 0.4583        | 0.9558 | 270  | 0.4654          | 0.9048   | 0.9057    | 0.9048 | 0.9048 | 0.4770 |
| 0.4734        | 0.9912 | 280  | 0.4656          | 0.9038   | 0.9047    | 0.9038 | 0.9038 | 0.4760 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1