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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: []
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 |