---
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: []
pipeline_tag: zero-shot-classification
---

<!-- 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: 0.7709
- Accuracy: 0.7875
- Precision: 0.5276
- Recall: 0.5256
- F1: 0.5261
- Ratio: 0.5083

## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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  |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 3.4508        | 0.1429 | 10   | 1.7714          | 0.5625   | 0.3772    | 0.3755 | 0.3758 | 0.5167 |
| 1.3131        | 0.2857 | 20   | 1.2703          | 0.6083   | 0.6073    | 0.6066 | 0.6066 | 0.4542 |
| 1.0393        | 0.4286 | 30   | 0.9366          | 0.6625   | 0.6623    | 0.6603 | 0.6604 | 0.4417 |
| 0.8129        | 0.5714 | 40   | 0.8434          | 0.7167   | 0.7179    | 0.7179 | 0.7167 | 0.5208 |
| 0.816         | 0.7143 | 50   | 0.9037          | 0.7042   | 0.7447    | 0.7122 | 0.6961 | 0.6833 |
| 0.7914        | 0.8571 | 60   | 0.7575          | 0.7583   | 0.7586    | 0.7569 | 0.7573 | 0.4542 |
| 0.7873        | 1.0    | 70   | 0.7795          | 0.75     | 0.7709    | 0.7555 | 0.7475 | 0.6208 |
| 0.6177        | 1.1429 | 80   | 0.7027          | 0.7917   | 0.7914    | 0.7910 | 0.7911 | 0.4708 |
| 0.5429        | 1.2857 | 90   | 0.7100          | 0.7917   | 0.7915    | 0.792  | 0.7915 | 0.4958 |
| 0.5314        | 1.4286 | 100  | 0.7451          | 0.7875   | 0.5276    | 0.5256 | 0.5261 | 0.5083 |
| 0.5945        | 1.5714 | 110  | 0.7605          | 0.8      | 0.5358    | 0.5324 | 0.5338 | 0.4542 |
| 0.661         | 1.7143 | 120  | 0.7722          | 0.7792   | 0.5215    | 0.5195 | 0.5204 | 0.4917 |
| 0.6144        | 1.8571 | 130  | 0.7688          | 0.7875   | 0.5273    | 0.5253 | 0.5260 | 0.5    |
| 0.5695        | 2.0    | 140  | 0.7709          | 0.7875   | 0.5276    | 0.5256 | 0.5261 | 0.5083 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1