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videberta-base_v1

Text Classification
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deberta-v2
Generated from Trainer
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  1. README.md +86 -0
  2. classification_report_test.txt +14 -0
  3. confusion_matrix_test.csv +4 -0
  4. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ base_model: Fsoft-AIC/videberta-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: videberta-base_v1
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # videberta-base_v1
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+
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+ This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4110
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+ - Accuracy: 0.8882
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+ - Precision Macro: 0.7636
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+ - Recall Macro: 0.7197
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+ - F1 Macro: 0.7363
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+ - F1 Weighted: 0.8843
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 128
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 20
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:|
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+ | 0.8764 | 1.0 | 90 | 0.7142 | 0.6974 | 0.4684 | 0.4901 | 0.4759 | 0.6809 |
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+ | 0.682 | 2.0 | 180 | 0.5610 | 0.7701 | 0.5261 | 0.5431 | 0.5253 | 0.7514 |
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+ | 0.5221 | 3.0 | 270 | 0.4966 | 0.8294 | 0.5546 | 0.5817 | 0.5660 | 0.8102 |
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+ | 0.429 | 4.0 | 360 | 0.4697 | 0.8395 | 0.6756 | 0.5881 | 0.5807 | 0.8204 |
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+ | 0.3652 | 5.0 | 450 | 0.4085 | 0.8642 | 0.7889 | 0.6334 | 0.6442 | 0.8503 |
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+ | 0.3638 | 6.0 | 540 | 0.4011 | 0.8743 | 0.8328 | 0.6359 | 0.6447 | 0.8591 |
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+ | 0.3148 | 7.0 | 630 | 0.3770 | 0.8806 | 0.8160 | 0.6770 | 0.7037 | 0.8712 |
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+ | 0.2928 | 8.0 | 720 | 0.3874 | 0.8825 | 0.8480 | 0.6751 | 0.7020 | 0.8724 |
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+ | 0.2705 | 9.0 | 810 | 0.3800 | 0.8793 | 0.7808 | 0.7026 | 0.7254 | 0.8737 |
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+ | 0.2397 | 10.0 | 900 | 0.3699 | 0.8882 | 0.8000 | 0.6991 | 0.7257 | 0.8810 |
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+ | 0.2325 | 11.0 | 990 | 0.3837 | 0.8863 | 0.8213 | 0.6647 | 0.6855 | 0.8745 |
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+ | 0.2158 | 12.0 | 1080 | 0.3721 | 0.8857 | 0.7843 | 0.7061 | 0.7296 | 0.8798 |
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+ | 0.1985 | 13.0 | 1170 | 0.3878 | 0.8907 | 0.8037 | 0.7090 | 0.7362 | 0.8844 |
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+ | 0.2035 | 14.0 | 1260 | 0.3784 | 0.8857 | 0.7685 | 0.7173 | 0.7363 | 0.8815 |
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+ | 0.1805 | 15.0 | 1350 | 0.4019 | 0.8850 | 0.7565 | 0.7005 | 0.7193 | 0.8795 |
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+ | 0.1808 | 16.0 | 1440 | 0.4085 | 0.8882 | 0.7732 | 0.7114 | 0.7322 | 0.8831 |
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+ | 0.1646 | 17.0 | 1530 | 0.3906 | 0.8831 | 0.7496 | 0.7368 | 0.7427 | 0.8819 |
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+ | 0.1687 | 18.0 | 1620 | 0.3998 | 0.8857 | 0.7606 | 0.7306 | 0.7431 | 0.8831 |
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+ | 0.1636 | 19.0 | 1710 | 0.4107 | 0.8863 | 0.7594 | 0.7184 | 0.7341 | 0.8826 |
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+ | 0.1634 | 20.0 | 1800 | 0.4110 | 0.8882 | 0.7636 | 0.7197 | 0.7363 | 0.8843 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.55.0
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+ - Pytorch 2.7.0+cu126
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+ - Datasets 4.0.0
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+ - Tokenizers 0.21.4
classification_report_test.txt ADDED
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+ precision recall f1-score support
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+
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+ negative 0.87 0.93 0.90 1409
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+ neutral 0.37 0.26 0.31 167
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+ positive 0.92 0.89 0.91 1590
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+
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+ accuracy 0.87 3166
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+ macro avg 0.72 0.69 0.70 3166
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+ weighted avg 0.87 0.87 0.87 3166
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+
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+ Confusion matrix:
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+ [[1311 30 68]
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+ [ 72 44 51]
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+ [ 130 46 1414]]
confusion_matrix_test.csv ADDED
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+ ,negative,neutral,positive
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+ negative,1311,30,68
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+ neutral,72,44,51
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+ positive,130,46,1414
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