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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: 0.6453
- Accuracy: 0.8309
- Precision: 0.8362
- Recall: 0.8309
- F1: 0.8302
- Ratio: 0.5631
## 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: 1
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.5106 | 0.1626 | 10 | 0.6599 | 0.8289 | 0.8500 | 0.8289 | 0.8262 | 0.3772 |
| 0.5228 | 0.3252 | 20 | 0.5642 | 0.8517 | 0.8517 | 0.8517 | 0.8517 | 0.5020 |
| 0.5035 | 0.4878 | 30 | 0.5669 | 0.8544 | 0.8554 | 0.8544 | 0.8543 | 0.4725 |
| 0.4325 | 0.6504 | 40 | 0.6077 | 0.8403 | 0.8442 | 0.8403 | 0.8398 | 0.4463 |
| 0.4902 | 0.8130 | 50 | 0.6391 | 0.8322 | 0.8369 | 0.8322 | 0.8316 | 0.5591 |
| 0.4774 | 0.9756 | 60 | 0.6453 | 0.8309 | 0.8362 | 0.8309 | 0.8302 | 0.5631 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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