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videberta-xsmall_v2

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-xsmall
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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-xsmall_v2
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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-xsmall_v2
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+
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+ This model is a fine-tuned version of [Fsoft-AIC/videberta-xsmall](https://huggingface.co/Fsoft-AIC/videberta-xsmall) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3825
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+ - Accuracy: 0.9021
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+ - Precision Macro: 0.7867
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+ - Recall Macro: 0.7246
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+ - F1 Macro: 0.7460
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+ - F1 Weighted: 0.8970
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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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+ | 0.8576 | 1.0 | 90 | 0.5078 | 0.8263 | 0.5549 | 0.5804 | 0.5639 | 0.8071 |
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+ | 0.462 | 2.0 | 180 | 0.4000 | 0.8667 | 0.5773 | 0.6061 | 0.5912 | 0.8464 |
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+ | 0.3895 | 3.0 | 270 | 0.3928 | 0.8711 | 0.5819 | 0.6073 | 0.5939 | 0.8503 |
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+ | 0.334 | 4.0 | 360 | 0.3652 | 0.8793 | 0.5871 | 0.6133 | 0.5996 | 0.8584 |
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+ | 0.2906 | 5.0 | 450 | 0.3542 | 0.8844 | 0.7567 | 0.6261 | 0.6205 | 0.8658 |
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+ | 0.2912 | 6.0 | 540 | 0.3562 | 0.8895 | 0.8269 | 0.6502 | 0.6619 | 0.8747 |
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+ | 0.2564 | 7.0 | 630 | 0.3404 | 0.8932 | 0.7944 | 0.7308 | 0.7537 | 0.8890 |
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+ | 0.2424 | 8.0 | 720 | 0.3381 | 0.8970 | 0.8492 | 0.6758 | 0.6998 | 0.8854 |
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+ | 0.2167 | 9.0 | 810 | 0.3292 | 0.9015 | 0.8294 | 0.7088 | 0.7377 | 0.8938 |
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+ | 0.2052 | 10.0 | 900 | 0.3621 | 0.9021 | 0.8239 | 0.7163 | 0.7459 | 0.8953 |
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+ | 0.1976 | 11.0 | 990 | 0.3453 | 0.9002 | 0.8251 | 0.7113 | 0.7408 | 0.8930 |
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+ | 0.1904 | 12.0 | 1080 | 0.3754 | 0.9015 | 0.8426 | 0.7040 | 0.7345 | 0.8931 |
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+ | 0.176 | 13.0 | 1170 | 0.3586 | 0.9046 | 0.8177 | 0.7101 | 0.7378 | 0.8971 |
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+ | 0.1783 | 14.0 | 1260 | 0.3635 | 0.8958 | 0.7590 | 0.7239 | 0.7379 | 0.8922 |
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+ | 0.1566 | 15.0 | 1350 | 0.4087 | 0.8926 | 0.7601 | 0.7089 | 0.7270 | 0.8874 |
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+ | 0.1509 | 16.0 | 1440 | 0.3878 | 0.9033 | 0.8019 | 0.7172 | 0.7427 | 0.8970 |
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+ | 0.1463 | 17.0 | 1530 | 0.3730 | 0.8989 | 0.7670 | 0.7348 | 0.7481 | 0.8959 |
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+ | 0.1493 | 18.0 | 1620 | 0.3801 | 0.8996 | 0.7687 | 0.7225 | 0.7397 | 0.8952 |
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+ | 0.1465 | 19.0 | 1710 | 0.3995 | 0.8983 | 0.7738 | 0.7175 | 0.7372 | 0.8932 |
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+ | 0.133 | 20.0 | 1800 | 0.3825 | 0.9021 | 0.7867 | 0.7246 | 0.7460 | 0.8970 |
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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 0.92 0.90 1409
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+ neutral 0.42 0.22 0.29 167
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+ positive 0.91 0.91 0.91 1590
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+
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+ accuracy 0.88 3166
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+ macro avg 0.73 0.68 0.70 3166
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+ weighted avg 0.87 0.88 0.87 3166
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+
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+ Confusion matrix:
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+ [[1299 25 85]
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+ [ 70 37 60]
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+ [ 122 26 1442]]
confusion_matrix_test.csv ADDED
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+ ,negative,neutral,positive
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+ negative,1299,25,85
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+ neutral,70,37,60
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+ positive,122,26,1442
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