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vi-bert-base_v1

Text Classification
Transformers
Safetensors
bert
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: FPTAI/vibert-base-cased
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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: vi-bert-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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+ # vi-bert-base_v1
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+
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+ This model is a fine-tuned version of [FPTAI/vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4995
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+ - Accuracy: 0.9292
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+ - Precision Macro: 0.8368
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+ - Recall Macro: 0.7769
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+ - F1 Macro: 0.8000
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+ - F1 Weighted: 0.9259
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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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+ | 0.5604 | 1.0 | 90 | 0.2596 | 0.9128 | 0.9000 | 0.6666 | 0.6788 | 0.8972 |
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+ | 0.2258 | 2.0 | 180 | 0.2182 | 0.9286 | 0.8216 | 0.8017 | 0.8109 | 0.9275 |
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+ | 0.1532 | 3.0 | 270 | 0.2312 | 0.9198 | 0.7940 | 0.7902 | 0.7919 | 0.9195 |
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+ | 0.123 | 4.0 | 360 | 0.2432 | 0.9311 | 0.8607 | 0.8000 | 0.8238 | 0.9286 |
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+ | 0.0785 | 5.0 | 450 | 0.2592 | 0.9255 | 0.8450 | 0.7784 | 0.8037 | 0.9222 |
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+ | 0.0628 | 6.0 | 540 | 0.3075 | 0.9280 | 0.8358 | 0.7765 | 0.7993 | 0.9247 |
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+ | 0.0457 | 7.0 | 630 | 0.3155 | 0.9255 | 0.8118 | 0.7996 | 0.8053 | 0.9247 |
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+ | 0.034 | 8.0 | 720 | 0.3924 | 0.9248 | 0.8212 | 0.7656 | 0.7870 | 0.9213 |
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+ | 0.0271 | 9.0 | 810 | 0.3776 | 0.9242 | 0.8211 | 0.7782 | 0.7957 | 0.9216 |
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+ | 0.0207 | 10.0 | 900 | 0.4209 | 0.9274 | 0.8067 | 0.8094 | 0.8080 | 0.9275 |
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+ | 0.0189 | 11.0 | 990 | 0.4373 | 0.9255 | 0.7988 | 0.7957 | 0.7971 | 0.9252 |
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+ | 0.0145 | 12.0 | 1080 | 0.4010 | 0.9349 | 0.8392 | 0.8228 | 0.8304 | 0.9341 |
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+ | 0.0083 | 13.0 | 1170 | 0.4337 | 0.9242 | 0.8237 | 0.7988 | 0.8100 | 0.9228 |
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+ | 0.004 | 14.0 | 1260 | 0.4571 | 0.9318 | 0.8491 | 0.7828 | 0.8080 | 0.9285 |
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+ | 0.0081 | 15.0 | 1350 | 0.4862 | 0.9286 | 0.8298 | 0.7857 | 0.8035 | 0.9261 |
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+ | 0.0027 | 16.0 | 1440 | 0.4788 | 0.9280 | 0.8348 | 0.7924 | 0.8103 | 0.9258 |
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+ | 0.0029 | 17.0 | 1530 | 0.4797 | 0.9305 | 0.8339 | 0.7903 | 0.8085 | 0.9281 |
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+ | 0.003 | 18.0 | 1620 | 0.4877 | 0.9280 | 0.8238 | 0.7807 | 0.7984 | 0.9253 |
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+ | 0.0013 | 19.0 | 1710 | 0.4966 | 0.9286 | 0.8363 | 0.7765 | 0.7996 | 0.9253 |
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+ | 0.0014 | 20.0 | 1800 | 0.4995 | 0.9292 | 0.8368 | 0.7769 | 0.8000 | 0.9259 |
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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 0.91 0.96 0.94 1409
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+ neutral 0.56 0.42 0.48 167
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+ positive 0.95 0.93 0.94 1590
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+
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+ accuracy 0.92 3166
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+ macro avg 0.81 0.77 0.78 3166
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+ weighted avg 0.91 0.92 0.91 3166
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+
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+ Confusion matrix:
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+ [[1350 21 38]
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+ [ 53 70 44]
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+ [ 75 34 1481]]
confusion_matrix_test.csv ADDED
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+ ,negative,neutral,positive
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+ negative,1350,21,38
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+ neutral,53,70,44
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+ positive,75,34,1481
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