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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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<!-- 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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# bert-base-multilingual-uncased-sentiment_v3 |
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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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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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### Training results |
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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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### Framework versions |
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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 |
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