---
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: stocks
  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. -->

# stocks

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: 0.6733
- Accuracy: 0.8109
- Precision: 0.8127
- Recall: 0.8109
- F1: 0.8107
- Ratio: 0.5378

## 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: 10
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 20
- 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: 2
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Ratio  |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 3.8156        | 0.1626 | 10   | 1.9553          | 0.5378   | 0.5507    | 0.5378 | 0.5064 | 0.7521 |
| 1.3339        | 0.3252 | 20   | 1.2090          | 0.5546   | 0.5548    | 0.5546 | 0.5543 | 0.5252 |
| 1.103         | 0.4878 | 30   | 0.9577          | 0.5588   | 0.5588    | 0.5588 | 0.5588 | 0.5042 |
| 0.9108        | 0.6504 | 40   | 0.8881          | 0.5714   | 0.5770    | 0.5714 | 0.5635 | 0.6345 |
| 0.8716        | 0.8130 | 50   | 0.8426          | 0.6387   | 0.6563    | 0.6387 | 0.6282 | 0.6681 |
| 0.844         | 0.9756 | 60   | 0.7948          | 0.7017   | 0.7233    | 0.7017 | 0.6943 | 0.3445 |
| 0.7816        | 1.1382 | 70   | 0.7715          | 0.7227   | 0.7660    | 0.7227 | 0.7109 | 0.7017 |
| 0.7406        | 1.3008 | 80   | 0.7040          | 0.8067   | 0.8099    | 0.8067 | 0.8062 | 0.5504 |
| 0.6764        | 1.4634 | 90   | 0.6954          | 0.8025   | 0.8104    | 0.8025 | 0.8013 | 0.5798 |
| 0.7306        | 1.6260 | 100  | 0.6933          | 0.8109   | 0.8209    | 0.8109 | 0.8094 | 0.5882 |
| 0.6736        | 1.7886 | 110  | 0.6763          | 0.8067   | 0.8089    | 0.8067 | 0.8064 | 0.5420 |
| 0.714         | 1.9512 | 120  | 0.6733          | 0.8109   | 0.8127    | 0.8109 | 0.8107 | 0.5378 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1