---
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: 0.6553
- Accuracy: 0.8101
- Precision: 0.8111
- Recall: 0.8101
- F1: 0.8099
- Ratio: 0.5289

## 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  |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 3.5199        | 0.1626 | 10   | 1.7420          | 0.5530   | 0.5581    | 0.5530 | 0.5431 | 0.6477 |
| 1.6995        | 0.3252 | 20   | 1.3228          | 0.5356   | 0.5554    | 0.5356 | 0.4899 | 0.2007 |
| 1.1579        | 0.4878 | 30   | 0.9331          | 0.5785   | 0.5796    | 0.5785 | 0.5771 | 0.4423 |
| 0.9588        | 0.6504 | 40   | 0.8592          | 0.6329   | 0.6340    | 0.6329 | 0.6321 | 0.5450 |
| 0.91          | 0.8130 | 50   | 0.8239          | 0.6738   | 0.7473    | 0.6738 | 0.6477 | 0.7725 |
| 0.8624        | 0.9756 | 60   | 0.8217          | 0.6      | 0.7217    | 0.6    | 0.5364 | 0.1295 |
| 0.8238        | 1.1382 | 70   | 0.7594          | 0.7477   | 0.7802    | 0.7477 | 0.7401 | 0.6705 |
| 0.7669        | 1.3008 | 80   | 0.6968          | 0.7913   | 0.7922    | 0.7913 | 0.7911 | 0.5289 |
| 0.7648        | 1.4634 | 90   | 0.6744          | 0.8007   | 0.8015    | 0.8007 | 0.8005 | 0.4738 |
| 0.691         | 1.6260 | 100  | 0.6739          | 0.7993   | 0.8029    | 0.7993 | 0.7987 | 0.5544 |
| 0.6698        | 1.7886 | 110  | 0.6616          | 0.8067   | 0.8091    | 0.8067 | 0.8063 | 0.5443 |
| 0.6985        | 1.9512 | 120  | 0.6553          | 0.8101   | 0.8111    | 0.8101 | 0.8099 | 0.5289 |


### Framework versions

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