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bert-base-multilingual-uncased-sentiment_v3

Text Classification
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bert
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  1. README.md +87 -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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+ license: mit
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+ base_model: nlptown/bert-base-multilingual-uncased-sentiment
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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: bert-base-multilingual-uncased-sentiment_v3
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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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+ # bert-base-multilingual-uncased-sentiment_v3
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+
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+ This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4987
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+ - Accuracy: 0.9286
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+ - Precision Macro: 0.8226
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+ - Recall Macro: 0.7931
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+ - F1 Macro: 0.8061
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+ - F1 Weighted: 0.9269
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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: 5e-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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+ | 0.3933 | 1.0 | 90 | 0.2349 | 0.9292 | 0.8484 | 0.7197 | 0.7474 | 0.9202 |
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+ | 0.2051 | 2.0 | 180 | 0.2166 | 0.9236 | 0.8134 | 0.7619 | 0.7811 | 0.9199 |
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+ | 0.1494 | 3.0 | 270 | 0.2369 | 0.9362 | 0.8619 | 0.7775 | 0.8072 | 0.9321 |
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+ | 0.1233 | 4.0 | 360 | 0.2290 | 0.9343 | 0.8660 | 0.7894 | 0.8176 | 0.9309 |
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+ | 0.0838 | 5.0 | 450 | 0.2490 | 0.9375 | 0.8610 | 0.8200 | 0.8378 | 0.9358 |
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+ | 0.0799 | 6.0 | 540 | 0.2579 | 0.9343 | 0.8528 | 0.7977 | 0.8197 | 0.9317 |
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+ | 0.0481 | 7.0 | 630 | 0.3494 | 0.9223 | 0.7926 | 0.8252 | 0.8064 | 0.9247 |
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+ | 0.0406 | 8.0 | 720 | 0.3154 | 0.9368 | 0.8591 | 0.7986 | 0.8227 | 0.9341 |
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+ | 0.032 | 9.0 | 810 | 0.3219 | 0.9305 | 0.8238 | 0.8153 | 0.8194 | 0.9301 |
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+ | 0.0333 | 10.0 | 900 | 0.3787 | 0.9286 | 0.8387 | 0.8048 | 0.8198 | 0.9270 |
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+ | 0.0278 | 11.0 | 990 | 0.3914 | 0.9311 | 0.8432 | 0.7948 | 0.8148 | 0.9288 |
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+ | 0.0165 | 12.0 | 1080 | 0.4155 | 0.9318 | 0.8627 | 0.7830 | 0.8120 | 0.9282 |
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+ | 0.0126 | 13.0 | 1170 | 0.4029 | 0.9368 | 0.8550 | 0.8161 | 0.8328 | 0.9352 |
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+ | 0.0133 | 14.0 | 1260 | 0.4398 | 0.9324 | 0.8460 | 0.7915 | 0.8134 | 0.9297 |
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+ | 0.01 | 15.0 | 1350 | 0.4571 | 0.9318 | 0.8347 | 0.7913 | 0.8094 | 0.9294 |
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+ | 0.008 | 16.0 | 1440 | 0.4685 | 0.9299 | 0.8303 | 0.7899 | 0.8070 | 0.9276 |
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+ | 0.0058 | 17.0 | 1530 | 0.4846 | 0.9318 | 0.8403 | 0.7954 | 0.8142 | 0.9295 |
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+ | 0.0022 | 18.0 | 1620 | 0.4905 | 0.9280 | 0.8249 | 0.7928 | 0.8068 | 0.9262 |
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+ | 0.0038 | 19.0 | 1710 | 0.5043 | 0.9299 | 0.8272 | 0.7897 | 0.8057 | 0.9277 |
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+ | 0.0015 | 20.0 | 1800 | 0.4987 | 0.9286 | 0.8226 | 0.7931 | 0.8061 | 0.9269 |
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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 0.95 0.93 1409
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+ neutral 0.57 0.39 0.46 167
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+ positive 0.94 0.93 0.94 1590
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+
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+ accuracy 0.91 3166
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+ macro avg 0.80 0.76 0.78 3166
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+ weighted avg 0.91 0.91 0.91 3166
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+
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+ Confusion matrix:
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+ [[1339 23 47]
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+ [ 51 65 51]
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+ [ 79 27 1484]]
confusion_matrix_test.csv ADDED
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+ ,negative,neutral,positive
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+ negative,1339,23,47
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+ neutral,51,65,51
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+ positive,79,27,1484
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