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RoBERTa-Base-SE2025T11A-sun-v20250108110637

Text Classification
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  1. README.md +123 -0
  2. config.json +29 -0
  3. merges.txt +0 -0
  4. model.safetensors +3 -0
  5. runs/Jan08_11-07-47_32df0236926e/events.out.tfevents.1736334474.32df0236926e.901.0 +3 -0
  6. special_tokens_map.json +51 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +58 -0
  9. training_args.bin +3 -0
  10. vocab.json +0 -0
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: w11wo/sundanese-roberta-base
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: RoBERTa-Base-SE2025T11A-sun-v20250108110637
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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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+ # RoBERTa-Base-SE2025T11A-sun-v20250108110637
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+
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+ This model is a fine-tuned version of [w11wo/sundanese-roberta-base](https://huggingface.co/w11wo/sundanese-roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5061
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+ - F1 Macro: 0.6108
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+ - F1 Micro: 0.6184
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+ - F1 Weighted: 0.6177
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+ - F1 Samples: 0.6281
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+ - F1 Label Marah: 0.6606
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+ - F1 Label Jijik: 0.5607
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+ - F1 Label Takut: 0.5833
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+ - F1 Label Senang: 0.7765
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+ - F1 Label Sedih: 0.5897
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+ - F1 Label Terkejut: 0.5641
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+ - F1 Label Biasa: 0.5405
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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: 2e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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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: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | F1 Weighted | F1 Samples | F1 Label Marah | F1 Label Jijik | F1 Label Takut | F1 Label Senang | F1 Label Sedih | F1 Label Terkejut | F1 Label Biasa |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:--------------:|:--------------:|:--------------:|:---------------:|:--------------:|:-----------------:|:--------------:|
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+ | 0.5025 | 0.1805 | 100 | 0.4545 | 0.0381 | 0.0539 | 0.0497 | 0.0345 | 0.1613 | 0.1053 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.4934 | 0.3610 | 200 | 0.4318 | 0.0204 | 0.0209 | 0.0198 | 0.0120 | 0.0 | 0.0 | 0.0541 | 0.0889 | 0.0 | 0.0 | 0.0 |
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+ | 0.4199 | 0.5415 | 300 | 0.4168 | 0.1191 | 0.2156 | 0.1225 | 0.1351 | 0.0 | 0.04 | 0.0 | 0.7473 | 0.0 | 0.0465 | 0.0 |
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+ | 0.4328 | 0.7220 | 400 | 0.3883 | 0.2034 | 0.2971 | 0.2179 | 0.1730 | 0.0357 | 0.2769 | 0.0 | 0.7273 | 0.0 | 0.3836 | 0.0 |
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+ | 0.4336 | 0.9025 | 500 | 0.3829 | 0.2447 | 0.3324 | 0.2520 | 0.2477 | 0.1667 | 0.1481 | 0.5312 | 0.7253 | 0.0952 | 0.0465 | 0.0 |
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+ | 0.3805 | 1.0830 | 600 | 0.3518 | 0.3722 | 0.4373 | 0.4013 | 0.3431 | 0.425 | 0.3733 | 0.4681 | 0.7209 | 0.4444 | 0.1739 | 0.0 |
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+ | 0.3449 | 1.2635 | 700 | 0.3677 | 0.4440 | 0.5119 | 0.4852 | 0.4492 | 0.5636 | 0.5138 | 0.5574 | 0.6933 | 0.2979 | 0.4819 | 0.0 |
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+ | 0.3215 | 1.4440 | 800 | 0.3446 | 0.3923 | 0.4552 | 0.4232 | 0.3844 | 0.3429 | 0.4706 | 0.3256 | 0.7407 | 0.5333 | 0.3333 | 0.0 |
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+ | 0.3339 | 1.6245 | 900 | 0.3218 | 0.5718 | 0.5844 | 0.5672 | 0.5326 | 0.6186 | 0.4304 | 0.6761 | 0.8 | 0.6098 | 0.2963 | 0.5714 |
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+ | 0.3285 | 1.8051 | 1000 | 0.3236 | 0.5582 | 0.5636 | 0.5451 | 0.4788 | 0.5060 | 0.2951 | 0.6780 | 0.7381 | 0.5538 | 0.5652 | 0.5714 |
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+ | 0.3138 | 1.9856 | 1100 | 0.3195 | 0.4983 | 0.5837 | 0.5460 | 0.5584 | 0.6486 | 0.58 | 0.56 | 0.8 | 0.4231 | 0.4762 | 0.0 |
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+ | 0.2243 | 2.1661 | 1200 | 0.3214 | 0.6046 | 0.6070 | 0.6087 | 0.5853 | 0.5745 | 0.5660 | 0.6774 | 0.7778 | 0.5806 | 0.5556 | 0.5 |
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+ | 0.2396 | 2.3466 | 1300 | 0.3096 | 0.5517 | 0.5821 | 0.5657 | 0.5488 | 0.6170 | 0.4058 | 0.6667 | 0.7792 | 0.5455 | 0.5143 | 0.3333 |
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+ | 0.214 | 2.5271 | 1400 | 0.3470 | 0.5485 | 0.5748 | 0.5626 | 0.5581 | 0.5116 | 0.5714 | 0.5926 | 0.7619 | 0.6377 | 0.4308 | 0.3333 |
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+ | 0.207 | 2.7076 | 1500 | 0.3219 | 0.5611 | 0.5963 | 0.5812 | 0.5689 | 0.68 | 0.4935 | 0.6557 | 0.7527 | 0.5 | 0.5122 | 0.3333 |
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+ | 0.2076 | 2.8881 | 1600 | 0.3184 | 0.5815 | 0.6260 | 0.6102 | 0.6147 | 0.6964 | 0.5783 | 0.6866 | 0.7674 | 0.5424 | 0.5385 | 0.2609 |
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+ | 0.1933 | 3.0686 | 1700 | 0.3289 | 0.6334 | 0.6338 | 0.6339 | 0.6171 | 0.6286 | 0.5909 | 0.6552 | 0.7368 | 0.5915 | 0.625 | 0.6061 |
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+ | 0.1382 | 3.2491 | 1800 | 0.3424 | 0.6222 | 0.6194 | 0.6111 | 0.6180 | 0.6239 | 0.48 | 0.64 | 0.7692 | 0.6111 | 0.525 | 0.7059 |
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+ | 0.1453 | 3.4296 | 1900 | 0.3447 | 0.6305 | 0.6267 | 0.6268 | 0.6159 | 0.6452 | 0.5490 | 0.6545 | 0.75 | 0.6154 | 0.5507 | 0.6486 |
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+ | 0.1299 | 3.6101 | 2000 | 0.3615 | 0.6390 | 0.6302 | 0.6303 | 0.6185 | 0.5833 | 0.5347 | 0.6857 | 0.8049 | 0.6 | 0.5979 | 0.6667 |
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+ | 0.1153 | 3.7906 | 2100 | 0.3538 | 0.6205 | 0.6257 | 0.6181 | 0.6254 | 0.6667 | 0.5301 | 0.72 | 0.7442 | 0.5312 | 0.5263 | 0.625 |
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+ | 0.1411 | 3.9711 | 2200 | 0.3747 | 0.6002 | 0.6018 | 0.5994 | 0.6195 | 0.6195 | 0.5524 | 0.6364 | 0.7381 | 0.5679 | 0.4928 | 0.5946 |
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+ | 0.1288 | 4.1516 | 2300 | 0.3738 | 0.6011 | 0.6063 | 0.6022 | 0.6020 | 0.6607 | 0.5057 | 0.5634 | 0.7529 | 0.6111 | 0.5195 | 0.5946 |
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+ | 0.0787 | 4.3321 | 2400 | 0.3761 | 0.6160 | 0.6239 | 0.6184 | 0.6138 | 0.6667 | 0.5060 | 0.6479 | 0.7765 | 0.6585 | 0.4938 | 0.5625 |
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+ | 0.077 | 4.5126 | 2500 | 0.3990 | 0.6063 | 0.6089 | 0.6104 | 0.5962 | 0.6226 | 0.5435 | 0.6579 | 0.7397 | 0.6087 | 0.5455 | 0.5263 |
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+ | 0.1005 | 4.6931 | 2600 | 0.4208 | 0.6186 | 0.6221 | 0.6229 | 0.6200 | 0.6796 | 0.5593 | 0.6780 | 0.75 | 0.5797 | 0.5195 | 0.5641 |
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+ | 0.0732 | 4.8736 | 2700 | 0.4226 | 0.6001 | 0.6089 | 0.6085 | 0.6086 | 0.66 | 0.5310 | 0.6071 | 0.7816 | 0.5946 | 0.5263 | 0.5 |
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+ | 0.0853 | 5.0542 | 2800 | 0.4128 | 0.6158 | 0.6216 | 0.6193 | 0.6189 | 0.6610 | 0.5657 | 0.6087 | 0.7561 | 0.5882 | 0.55 | 0.5806 |
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+ | 0.0623 | 5.2347 | 2900 | 0.4176 | 0.6103 | 0.6180 | 0.6153 | 0.6104 | 0.6538 | 0.5495 | 0.5758 | 0.7529 | 0.6316 | 0.5526 | 0.5556 |
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+ | 0.0569 | 5.4152 | 3000 | 0.4258 | 0.6224 | 0.6287 | 0.6231 | 0.6276 | 0.6847 | 0.5195 | 0.6585 | 0.7561 | 0.575 | 0.5570 | 0.6061 |
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+ | 0.0622 | 5.5957 | 3100 | 0.4234 | 0.6106 | 0.6194 | 0.6168 | 0.6171 | 0.6727 | 0.5714 | 0.5926 | 0.7619 | 0.6133 | 0.5063 | 0.5556 |
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+ | 0.0549 | 5.7762 | 3200 | 0.4328 | 0.6094 | 0.6148 | 0.6137 | 0.6096 | 0.6667 | 0.5625 | 0.5672 | 0.7561 | 0.6216 | 0.5128 | 0.5789 |
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+ | 0.0539 | 5.9567 | 3300 | 0.4466 | 0.6269 | 0.6223 | 0.6247 | 0.6152 | 0.6538 | 0.5636 | 0.6269 | 0.7595 | 0.6024 | 0.5366 | 0.6452 |
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+ | 0.0385 | 6.1372 | 3400 | 0.4408 | 0.6165 | 0.6207 | 0.6198 | 0.6233 | 0.6607 | 0.5660 | 0.6562 | 0.7470 | 0.5946 | 0.5195 | 0.5714 |
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+ | 0.0362 | 6.3177 | 3500 | 0.4625 | 0.6293 | 0.6335 | 0.6338 | 0.6333 | 0.6852 | 0.5983 | 0.6441 | 0.7619 | 0.6154 | 0.5122 | 0.5882 |
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+ | 0.042 | 6.4982 | 3600 | 0.4434 | 0.5992 | 0.6036 | 0.6028 | 0.5979 | 0.6275 | 0.5393 | 0.64 | 0.7561 | 0.5412 | 0.55 | 0.5405 |
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+ | 0.0483 | 6.6787 | 3700 | 0.4462 | 0.6225 | 0.6339 | 0.6308 | 0.6299 | 0.7091 | 0.5652 | 0.5882 | 0.7907 | 0.6053 | 0.5432 | 0.5556 |
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+ | 0.0359 | 6.8592 | 3800 | 0.4507 | 0.5981 | 0.6119 | 0.6127 | 0.6039 | 0.6796 | 0.5556 | 0.5882 | 0.7619 | 0.6377 | 0.5195 | 0.4444 |
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+ | 0.0385 | 7.0397 | 3900 | 0.4697 | 0.6224 | 0.625 | 0.6252 | 0.6246 | 0.6415 | 0.5818 | 0.5970 | 0.7561 | 0.6173 | 0.575 | 0.5882 |
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+ | 0.0289 | 7.2202 | 4000 | 0.4706 | 0.6201 | 0.6228 | 0.6222 | 0.6276 | 0.6607 | 0.5686 | 0.5797 | 0.7470 | 0.6173 | 0.5610 | 0.6061 |
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+ | 0.0299 | 7.4007 | 4100 | 0.4745 | 0.6103 | 0.6115 | 0.6111 | 0.6044 | 0.6182 | 0.5941 | 0.5833 | 0.7317 | 0.575 | 0.5641 | 0.6061 |
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+ | 0.0291 | 7.5812 | 4200 | 0.4836 | 0.6008 | 0.6098 | 0.6087 | 0.6119 | 0.6337 | 0.5794 | 0.5882 | 0.7765 | 0.5833 | 0.5316 | 0.5128 |
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+ | 0.0275 | 7.7617 | 4300 | 0.4867 | 0.6274 | 0.6323 | 0.6322 | 0.6327 | 0.6604 | 0.6168 | 0.6061 | 0.7470 | 0.5882 | 0.5854 | 0.5882 |
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+ | 0.0214 | 7.9422 | 4400 | 0.4874 | 0.6066 | 0.6128 | 0.6118 | 0.6123 | 0.6346 | 0.5490 | 0.5714 | 0.7765 | 0.6286 | 0.5455 | 0.5405 |
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+ | 0.02 | 8.1227 | 4500 | 0.4903 | 0.6247 | 0.6308 | 0.6290 | 0.6351 | 0.6429 | 0.5981 | 0.6087 | 0.7765 | 0.6301 | 0.5455 | 0.5714 |
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+ | 0.0194 | 8.3032 | 4600 | 0.4970 | 0.6090 | 0.6098 | 0.6094 | 0.6182 | 0.6207 | 0.5437 | 0.6027 | 0.7619 | 0.5814 | 0.5641 | 0.5882 |
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+ | 0.0213 | 8.4838 | 4700 | 0.4997 | 0.6072 | 0.6168 | 0.6161 | 0.6206 | 0.6538 | 0.5794 | 0.5882 | 0.7765 | 0.6076 | 0.5316 | 0.5128 |
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+ | 0.0171 | 8.6643 | 4800 | 0.4990 | 0.6102 | 0.6159 | 0.6136 | 0.6218 | 0.6355 | 0.5872 | 0.6087 | 0.7765 | 0.5854 | 0.5070 | 0.5714 |
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+ | 0.0208 | 8.8448 | 4900 | 0.5087 | 0.6052 | 0.6084 | 0.6070 | 0.6227 | 0.6154 | 0.5766 | 0.5915 | 0.7711 | 0.5823 | 0.5205 | 0.5789 |
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+ | 0.0194 | 9.0253 | 5000 | 0.5037 | 0.6100 | 0.6179 | 0.6174 | 0.6228 | 0.6667 | 0.5766 | 0.5882 | 0.7619 | 0.6111 | 0.525 | 0.5405 |
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+ | 0.0132 | 9.2058 | 5100 | 0.4986 | 0.5967 | 0.6053 | 0.6043 | 0.6138 | 0.6545 | 0.5660 | 0.5797 | 0.7619 | 0.5823 | 0.5063 | 0.5263 |
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+ | 0.0177 | 9.3863 | 5200 | 0.5023 | 0.5995 | 0.6085 | 0.6065 | 0.6207 | 0.6486 | 0.5607 | 0.5882 | 0.7765 | 0.5897 | 0.5067 | 0.5263 |
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+ | 0.0138 | 9.5668 | 5300 | 0.5026 | 0.6087 | 0.6168 | 0.6156 | 0.6258 | 0.6606 | 0.5607 | 0.5797 | 0.7765 | 0.5974 | 0.5455 | 0.5405 |
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+ | 0.0161 | 9.7473 | 5400 | 0.5066 | 0.6123 | 0.6195 | 0.6185 | 0.6273 | 0.6606 | 0.5741 | 0.5797 | 0.7765 | 0.5897 | 0.55 | 0.5556 |
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+ | 0.0155 | 9.9278 | 5500 | 0.5061 | 0.6108 | 0.6184 | 0.6177 | 0.6281 | 0.6606 | 0.5607 | 0.5833 | 0.7765 | 0.5897 | 0.5641 | 0.5405 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.47.1
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+ - Pytorch 2.5.1+cu121
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
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