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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: SYN_300524_epoch_1
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. -->
# SYN_300524_epoch_1
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: 0.3748
- Accuracy: 0.961
- Precision: 0.9615
- Recall: 0.961
- F1: 0.9610
- Ratio: 0.483
## 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: 16
- eval_batch_size: 16
- seed: 47
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----:|
| 2.9367 | 0.0533 | 10 | 1.4668 | 0.65 | 0.7117 | 0.65 | 0.6225 | 0.77 |
| 1.1009 | 0.1067 | 20 | 0.6674 | 0.856 | 0.8560 | 0.856 | 0.8560 | 0.502 |
| 0.6993 | 0.16 | 30 | 0.5583 | 0.908 | 0.9095 | 0.9080 | 0.9079 | 0.53 |
| 0.6377 | 0.2133 | 40 | 0.4923 | 0.934 | 0.9343 | 0.9340 | 0.9340 | 0.486 |
| 0.5192 | 0.2667 | 50 | 0.4930 | 0.926 | 0.9282 | 0.9260 | 0.9259 | 0.464 |
| 0.5189 | 0.32 | 60 | 0.4687 | 0.937 | 0.9383 | 0.937 | 0.9370 | 0.527 |
| 0.5083 | 0.3733 | 70 | 0.4321 | 0.944 | 0.9445 | 0.944 | 0.9440 | 0.484 |
| 0.4645 | 0.4267 | 80 | 0.4026 | 0.949 | 0.9490 | 0.949 | 0.9490 | 0.495 |
| 0.4268 | 0.48 | 90 | 0.3990 | 0.949 | 0.9498 | 0.9490 | 0.9490 | 0.479 |
| 0.4327 | 0.5333 | 100 | 0.3949 | 0.952 | 0.9524 | 0.952 | 0.9520 | 0.486 |
| 0.4283 | 0.5867 | 110 | 0.3894 | 0.954 | 0.9540 | 0.954 | 0.9540 | 0.496 |
| 0.4263 | 0.64 | 120 | 0.3829 | 0.957 | 0.9572 | 0.957 | 0.9570 | 0.489 |
| 0.4205 | 0.6933 | 130 | 0.3800 | 0.962 | 0.9620 | 0.962 | 0.9620 | 0.496 |
| 0.4291 | 0.7467 | 140 | 0.3760 | 0.962 | 0.9620 | 0.962 | 0.9620 | 0.502 |
| 0.4124 | 0.8 | 150 | 0.3723 | 0.964 | 0.9641 | 0.964 | 0.9640 | 0.494 |
| 0.4142 | 0.8533 | 160 | 0.3720 | 0.964 | 0.9641 | 0.964 | 0.9640 | 0.492 |
| 0.4209 | 0.9067 | 170 | 0.3767 | 0.96 | 0.9605 | 0.96 | 0.9600 | 0.484 |
| 0.3908 | 0.96 | 180 | 0.3748 | 0.96 | 0.9605 | 0.96 | 0.9600 | 0.484 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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