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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: 2504v3
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. -->
# 2504v3
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: 0.6951
- Accuracy: 0.8487
- Precision: 0.8488
- Recall: 0.8487
- F1: 0.8487
- Ratio: 0.4916
## 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
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 5.617 | 0.1626 | 10 | 5.2818 | 0.1471 | 0.4233 | 0.0980 | 0.1518 | 0.1891 |
| 2.9819 | 0.3252 | 20 | 1.8921 | 0.5462 | 0.3817 | 0.3641 | 0.3655 | 0.6134 |
| 1.4506 | 0.4878 | 30 | 1.3671 | 0.5378 | 0.5459 | 0.5378 | 0.5165 | 0.2899 |
| 1.112 | 0.6504 | 40 | 0.8974 | 0.6261 | 0.6268 | 0.6261 | 0.6255 | 0.4622 |
| 0.872 | 0.8130 | 50 | 0.7909 | 0.7017 | 0.7320 | 0.7017 | 0.6916 | 0.6807 |
| 0.8282 | 0.9756 | 60 | 0.7232 | 0.7605 | 0.7614 | 0.7605 | 0.7603 | 0.4706 |
| 0.7528 | 1.1382 | 70 | 0.6917 | 0.7647 | 0.7654 | 0.7647 | 0.7646 | 0.5252 |
| 0.7292 | 1.3008 | 80 | 0.6830 | 0.7773 | 0.7789 | 0.7773 | 0.7770 | 0.5378 |
| 0.6003 | 1.4634 | 90 | 0.6686 | 0.7857 | 0.7968 | 0.7857 | 0.7837 | 0.5966 |
| 0.6511 | 1.6260 | 100 | 0.6301 | 0.8067 | 0.8071 | 0.8067 | 0.8067 | 0.5168 |
| 0.5804 | 1.7886 | 110 | 0.6498 | 0.7983 | 0.8004 | 0.7983 | 0.7980 | 0.4580 |
| 0.6096 | 1.9512 | 120 | 0.6107 | 0.8151 | 0.8152 | 0.8151 | 0.8151 | 0.5084 |
| 0.6082 | 2.1138 | 130 | 0.6035 | 0.8277 | 0.8283 | 0.8277 | 0.8277 | 0.4790 |
| 0.5099 | 2.2764 | 140 | 0.6308 | 0.8151 | 0.8155 | 0.8151 | 0.8151 | 0.5168 |
| 0.5049 | 2.4390 | 150 | 0.6372 | 0.8361 | 0.8381 | 0.8361 | 0.8359 | 0.5378 |
| 0.4987 | 2.6016 | 160 | 0.6228 | 0.8445 | 0.8446 | 0.8445 | 0.8445 | 0.5042 |
| 0.6128 | 2.7642 | 170 | 0.6122 | 0.8487 | 0.8488 | 0.8487 | 0.8487 | 0.4916 |
| 0.5384 | 2.9268 | 180 | 0.6065 | 0.8277 | 0.8346 | 0.8277 | 0.8268 | 0.5714 |
| 0.4899 | 3.0894 | 190 | 0.6652 | 0.8151 | 0.8195 | 0.8151 | 0.8145 | 0.4412 |
| 0.4299 | 3.2520 | 200 | 0.6596 | 0.8487 | 0.8512 | 0.8487 | 0.8485 | 0.5420 |
| 0.4523 | 3.4146 | 210 | 0.7557 | 0.8067 | 0.8110 | 0.8067 | 0.8061 | 0.4412 |
| 0.4542 | 3.5772 | 220 | 0.6954 | 0.8277 | 0.8283 | 0.8277 | 0.8277 | 0.4790 |
| 0.4587 | 3.7398 | 230 | 0.6812 | 0.8319 | 0.8323 | 0.8319 | 0.8319 | 0.4832 |
| 0.4816 | 3.9024 | 240 | 0.6309 | 0.8613 | 0.8634 | 0.8613 | 0.8611 | 0.5378 |
| 0.4866 | 4.0650 | 250 | 0.6423 | 0.8487 | 0.8503 | 0.8487 | 0.8486 | 0.5336 |
| 0.363 | 4.2276 | 260 | 0.6763 | 0.8445 | 0.8448 | 0.8445 | 0.8445 | 0.5126 |
| 0.399 | 4.3902 | 270 | 0.7227 | 0.8361 | 0.8367 | 0.8361 | 0.8361 | 0.4790 |
| 0.3862 | 4.5528 | 280 | 0.6777 | 0.8445 | 0.8448 | 0.8445 | 0.8445 | 0.5126 |
| 0.4815 | 4.7154 | 290 | 0.6559 | 0.8529 | 0.8532 | 0.8529 | 0.8529 | 0.5126 |
| 0.4548 | 4.8780 | 300 | 0.6757 | 0.8403 | 0.8451 | 0.8403 | 0.8398 | 0.4412 |
| 0.3675 | 5.0407 | 310 | 0.6526 | 0.8487 | 0.8491 | 0.8487 | 0.8487 | 0.5168 |
| 0.3626 | 5.2033 | 320 | 0.6815 | 0.8529 | 0.8532 | 0.8529 | 0.8529 | 0.5126 |
| 0.4256 | 5.3659 | 330 | 0.6904 | 0.8529 | 0.8532 | 0.8529 | 0.8529 | 0.4874 |
| 0.4515 | 5.5285 | 340 | 0.6561 | 0.8487 | 0.8496 | 0.8487 | 0.8486 | 0.5252 |
| 0.3661 | 5.6911 | 350 | 0.6681 | 0.8487 | 0.8491 | 0.8487 | 0.8487 | 0.5168 |
| 0.3792 | 5.8537 | 360 | 0.6740 | 0.8487 | 0.8487 | 0.8487 | 0.8487 | 0.5 |
| 0.4327 | 6.0163 | 370 | 0.6649 | 0.8487 | 0.8487 | 0.8487 | 0.8487 | 0.5 |
| 0.3426 | 6.1789 | 380 | 0.6462 | 0.8487 | 0.8503 | 0.8487 | 0.8486 | 0.5336 |
| 0.3329 | 6.3415 | 390 | 0.6767 | 0.8529 | 0.8550 | 0.8529 | 0.8527 | 0.5378 |
| 0.415 | 6.5041 | 400 | 0.7001 | 0.8445 | 0.8448 | 0.8445 | 0.8445 | 0.4874 |
| 0.388 | 6.6667 | 410 | 0.7217 | 0.8445 | 0.8457 | 0.8445 | 0.8444 | 0.4706 |
| 0.3585 | 6.8293 | 420 | 0.7232 | 0.8445 | 0.8457 | 0.8445 | 0.8444 | 0.4706 |
| 0.3657 | 6.9919 | 430 | 0.6943 | 0.8487 | 0.8496 | 0.8487 | 0.8486 | 0.4748 |
| 0.3366 | 7.1545 | 440 | 0.6999 | 0.8529 | 0.8536 | 0.8529 | 0.8529 | 0.4790 |
| 0.3497 | 7.3171 | 450 | 0.6797 | 0.8613 | 0.8614 | 0.8613 | 0.8613 | 0.5042 |
| 0.3219 | 7.4797 | 460 | 0.6905 | 0.8487 | 0.8496 | 0.8487 | 0.8486 | 0.5252 |
| 0.3459 | 7.6423 | 470 | 0.6872 | 0.8613 | 0.8614 | 0.8613 | 0.8613 | 0.5042 |
| 0.3669 | 7.8049 | 480 | 0.6941 | 0.8529 | 0.8536 | 0.8529 | 0.8529 | 0.4790 |
| 0.3888 | 7.9675 | 490 | 0.7014 | 0.8487 | 0.8496 | 0.8487 | 0.8486 | 0.4748 |
| 0.2989 | 8.1301 | 500 | 0.6951 | 0.8487 | 0.8488 | 0.8487 | 0.8487 | 0.4916 |
| 0.3743 | 8.2927 | 510 | 0.7026 | 0.8487 | 0.8488 | 0.8487 | 0.8487 | 0.4916 |
| 0.3086 | 8.4553 | 520 | 0.7182 | 0.8529 | 0.8532 | 0.8529 | 0.8529 | 0.4874 |
| 0.3251 | 8.6179 | 530 | 0.7135 | 0.8529 | 0.8532 | 0.8529 | 0.8529 | 0.4874 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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