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te-zsc-hybrid_VIC

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metadata
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: VICH_300524_epoch_3
    results: []

VICH_300524_epoch_3

This model is a fine-tuned version of projecte-aina/roberta-base-ca-v2-cased-te on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3866
  • Accuracy: 0.954
  • Precision: 0.9552
  • Recall: 0.954
  • F1: 0.9540
  • Ratio: 0.474

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
0.339 0.0157 10 0.4216 0.945 0.9453 0.9450 0.9450 0.487
0.3573 0.0314 20 0.4397 0.943 0.9430 0.943 0.9430 0.501
0.4019 0.0472 30 0.4330 0.945 0.9452 0.9450 0.9450 0.489
0.3443 0.0629 40 0.4368 0.942 0.9434 0.942 0.9420 0.472
0.3805 0.0786 50 0.4335 0.933 0.9331 0.933 0.9330 0.507
0.3837 0.0943 60 0.4273 0.938 0.9380 0.938 0.9380 0.498
0.3428 0.1101 70 0.4313 0.94 0.9403 0.94 0.9400 0.488
0.3954 0.1258 80 0.4323 0.945 0.9458 0.9450 0.9450 0.479
0.4144 0.1415 90 0.4299 0.94 0.9400 0.94 0.9400 0.502
0.3481 0.1572 100 0.4249 0.939 0.9391 0.9390 0.9390 0.491
0.3825 0.1730 110 0.4293 0.942 0.9420 0.942 0.9420 0.498
0.3605 0.1887 120 0.4130 0.949 0.9498 0.9490 0.9490 0.479
0.4028 0.2044 130 0.4105 0.948 0.9490 0.948 0.9480 0.476
0.3729 0.2201 140 0.4324 0.939 0.9391 0.9390 0.9390 0.507
0.3611 0.2358 150 0.4255 0.937 0.9371 0.937 0.9370 0.491
0.3683 0.2516 160 0.4290 0.943 0.9443 0.9430 0.9430 0.473
0.351 0.2673 170 0.4215 0.942 0.9426 0.942 0.9420 0.482
0.3697 0.2830 180 0.4280 0.944 0.9441 0.944 0.9440 0.492
0.3851 0.2987 190 0.4251 0.945 0.9461 0.9450 0.9450 0.475
0.335 0.3145 200 0.4276 0.945 0.9455 0.9450 0.9450 0.483
0.3744 0.3302 210 0.4173 0.947 0.9476 0.9470 0.9470 0.481
0.376 0.3459 220 0.4080 0.947 0.9478 0.9470 0.9470 0.479
0.3856 0.3616 230 0.4131 0.947 0.9472 0.9470 0.9470 0.489
0.4036 0.3774 240 0.4285 0.937 0.9370 0.937 0.9370 0.503
0.3863 0.3931 250 0.4159 0.939 0.9396 0.9390 0.9390 0.481
0.3619 0.4088 260 0.4212 0.944 0.9446 0.944 0.9440 0.482
0.4042 0.4245 270 0.4233 0.941 0.9411 0.9410 0.9410 0.493
0.3783 0.4403 280 0.4153 0.939 0.9390 0.9390 0.9390 0.505
0.3744 0.4560 290 0.4170 0.943 0.9447 0.9430 0.9429 0.469
0.4052 0.4717 300 0.4219 0.94 0.9423 0.94 0.9399 0.464
0.3531 0.4874 310 0.4049 0.949 0.9493 0.9490 0.9490 0.487
0.3812 0.5031 320 0.4042 0.951 0.9520 0.9510 0.9510 0.477
0.3587 0.5189 330 0.4030 0.95 0.9509 0.95 0.9500 0.478
0.3455 0.5346 340 0.4007 0.951 0.9512 0.951 0.9510 0.489
0.4174 0.5503 350 0.3989 0.952 0.9525 0.952 0.9520 0.484
0.4173 0.5660 360 0.4004 0.948 0.9487 0.948 0.9480 0.48
0.4012 0.5818 370 0.3956 0.95 0.9504 0.95 0.9500 0.486
0.388 0.5975 380 0.3968 0.949 0.9490 0.949 0.9490 0.495
0.3613 0.6132 390 0.3978 0.948 0.9482 0.948 0.9480 0.49
0.3699 0.6289 400 0.3988 0.956 0.9563 0.956 0.9560 0.488
0.3585 0.6447 410 0.3967 0.956 0.9569 0.956 0.9560 0.478
0.4017 0.6604 420 0.3888 0.959 0.9595 0.959 0.9590 0.483
0.3657 0.6761 430 0.3898 0.954 0.9541 0.954 0.9540 0.494
0.413 0.6918 440 0.3923 0.955 0.9550 0.955 0.9550 0.499
0.3977 0.7075 450 0.3884 0.955 0.9551 0.955 0.9550 0.491
0.4066 0.7233 460 0.3869 0.959 0.9593 0.959 0.9590 0.487
0.3908 0.7390 470 0.3878 0.956 0.9561 0.956 0.9560 0.492
0.4041 0.7547 480 0.3872 0.958 0.9584 0.958 0.9580 0.486
0.4191 0.7704 490 0.3945 0.952 0.9534 0.952 0.9520 0.472
0.3443 0.7862 500 0.3932 0.949 0.9500 0.9490 0.9490 0.477
0.3735 0.8019 510 0.3934 0.955 0.9552 0.955 0.9550 0.489
0.3913 0.8176 520 0.3965 0.954 0.9541 0.954 0.9540 0.494
0.4038 0.8333 530 0.3949 0.953 0.9531 0.953 0.9530 0.493
0.4055 0.8491 540 0.3933 0.952 0.9524 0.952 0.9520 0.486
0.4073 0.8648 550 0.3932 0.954 0.9546 0.954 0.9540 0.482
0.4471 0.8805 560 0.3944 0.952 0.9532 0.952 0.9520 0.474
0.4098 0.8962 570 0.3942 0.951 0.9525 0.9510 0.9510 0.471
0.4512 0.9119 580 0.3933 0.952 0.9534 0.952 0.9520 0.472
0.4309 0.9277 590 0.3914 0.952 0.9534 0.952 0.9520 0.472
0.3962 0.9434 600 0.3894 0.953 0.9543 0.9530 0.9530 0.473
0.4242 0.9591 610 0.3878 0.953 0.9543 0.9530 0.9530 0.473
0.3824 0.9748 620 0.3869 0.954 0.9552 0.954 0.9540 0.474
0.3837 0.9906 630 0.3867 0.954 0.9552 0.954 0.9540 0.474

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1