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Ahmed-ibn-Harun
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BrainHermorrhage-vit-base

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
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  1. README.md +308 -0
  2. all_results.json +8 -0
  3. config.json +32 -0
  4. model.safetensors +3 -0
  5. preprocessor_config.json +23 -0
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ base_model: google/vit-base-patch16-224-in21k
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+ tags:
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+ - image-classification
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: vit-base
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: Ahmed-ibn-Harun/BrainHermorrhage
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+ type: imagefolder
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+ config: default
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+ split: validation
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8261405672009864
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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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+ # vit-base
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Ahmed-ibn-Harun/BrainHermorrhage dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3755
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+ - Accuracy: 0.8261
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+ - Sensitivity: 0.7221
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+ - Specificity: 0.9289
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+ - F1 Score: 0.8050
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+ - Auc: 0.9162
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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: 0.0003
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.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: 50
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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 | Sensitivity | Specificity | F1 Score | Auc |
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+ |:-------------:|:-------:|:-----:|:---------------:|:--------:|:-----------:|:-----------:|:--------:|:------:|
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+ | 0.3331 | 0.2188 | 100 | 0.4220 | 0.7891 | 0.6868 | 0.8902 | 0.7641 | 0.8947 |
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+ | 0.4904 | 0.4376 | 200 | 0.4409 | 0.8038 | 0.7750 | 0.8324 | 0.7971 | 0.8931 |
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+ | 0.4875 | 0.6565 | 300 | 0.5088 | 0.8162 | 0.9009 | 0.7324 | 0.8298 | 0.9057 |
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+ | 0.4366 | 0.8753 | 400 | 0.3726 | 0.8314 | 0.7671 | 0.8951 | 0.8190 | 0.9190 |
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+ | 0.4663 | 1.0941 | 500 | 0.4225 | 0.8157 | 0.8910 | 0.7412 | 0.8278 | 0.9201 |
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+ | 0.2961 | 1.3129 | 600 | 0.3632 | 0.8339 | 0.7929 | 0.8745 | 0.8260 | 0.9244 |
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+ | 0.3367 | 1.5317 | 700 | 0.4454 | 0.8117 | 0.8870 | 0.7373 | 0.8241 | 0.9083 |
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+ | 0.4084 | 1.7505 | 800 | 0.5469 | 0.7408 | 0.9732 | 0.5108 | 0.7888 | 0.9068 |
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+ | 0.3161 | 1.9694 | 900 | 0.3893 | 0.8329 | 0.8672 | 0.7990 | 0.8377 | 0.9282 |
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+ | 0.4762 | 2.1882 | 1000 | 0.4871 | 0.7363 | 0.9633 | 0.5118 | 0.7842 | 0.8974 |
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+ | 0.4006 | 2.4070 | 1100 | 0.4228 | 0.7900 | 0.9326 | 0.6490 | 0.8154 | 0.9257 |
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+ | 0.4541 | 2.6258 | 1200 | 0.3389 | 0.8487 | 0.7641 | 0.9324 | 0.8340 | 0.9349 |
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+ | 0.5397 | 2.8446 | 1300 | 0.4587 | 0.7565 | 0.9732 | 0.5422 | 0.7990 | 0.9244 |
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+ | 0.2115 | 3.0635 | 1400 | 0.3976 | 0.8344 | 0.8196 | 0.8490 | 0.8312 | 0.9223 |
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+ | 0.3588 | 3.2823 | 1500 | 0.3928 | 0.8211 | 0.8949 | 0.7480 | 0.8326 | 0.9208 |
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+ | 0.3377 | 3.5011 | 1600 | 0.3943 | 0.8157 | 0.7483 | 0.8824 | 0.8015 | 0.9128 |
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+ | 0.3385 | 3.7199 | 1700 | 0.3627 | 0.8428 | 0.8256 | 0.8598 | 0.8393 | 0.9247 |
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+ | 0.3793 | 3.9387 | 1800 | 0.4015 | 0.8063 | 0.7592 | 0.8529 | 0.7958 | 0.9007 |
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+ | 0.2774 | 4.1575 | 1900 | 0.4174 | 0.8186 | 0.8018 | 0.8353 | 0.8147 | 0.9090 |
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+ | 0.2964 | 4.3764 | 2000 | 0.4120 | 0.8245 | 0.8940 | 0.7559 | 0.8352 | 0.9243 |
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+ | 0.2042 | 4.5952 | 2100 | 0.3984 | 0.8383 | 0.8414 | 0.8353 | 0.8381 | 0.9214 |
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+ | 0.2336 | 4.8140 | 2200 | 0.4263 | 0.8241 | 0.8722 | 0.7765 | 0.8314 | 0.9242 |
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+ | 0.2292 | 5.0328 | 2300 | 0.4430 | 0.8339 | 0.8186 | 0.8490 | 0.8306 | 0.9241 |
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+ | 0.265 | 5.2516 | 2400 | 0.4647 | 0.8314 | 0.7681 | 0.8941 | 0.8192 | 0.9204 |
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+ | 0.2754 | 5.4705 | 2500 | 0.5476 | 0.7886 | 0.9128 | 0.6657 | 0.8111 | 0.9116 |
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+ | 0.1859 | 5.6893 | 2600 | 0.4330 | 0.8324 | 0.8414 | 0.8235 | 0.8332 | 0.9218 |
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+ | 0.1785 | 5.9081 | 2700 | 0.4180 | 0.8369 | 0.8375 | 0.8363 | 0.8362 | 0.9199 |
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+ | 0.2057 | 6.1269 | 2800 | 0.4660 | 0.8319 | 0.8543 | 0.8098 | 0.8349 | 0.9158 |
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+ | 0.289 | 6.3457 | 2900 | 0.4399 | 0.8186 | 0.8196 | 0.8176 | 0.8180 | 0.9086 |
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+ | 0.1172 | 6.5646 | 3000 | 0.5597 | 0.8260 | 0.8474 | 0.8049 | 0.8289 | 0.9085 |
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+ | 0.1758 | 6.7834 | 3100 | 0.4902 | 0.8201 | 0.8335 | 0.8069 | 0.8217 | 0.9078 |
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+ | 0.2185 | 7.0022 | 3200 | 0.4738 | 0.8211 | 0.8295 | 0.8127 | 0.8218 | 0.9108 |
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+ | 0.2255 | 7.2210 | 3300 | 0.5072 | 0.8098 | 0.8771 | 0.7431 | 0.8210 | 0.9082 |
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+ | 0.213 | 7.4398 | 3400 | 0.4879 | 0.8379 | 0.7919 | 0.8833 | 0.8293 | 0.9126 |
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+ | 0.1528 | 7.6586 | 3500 | 0.6054 | 0.8137 | 0.8276 | 0.8 | 0.8154 | 0.9057 |
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+ | 0.1834 | 7.8775 | 3600 | 0.5653 | 0.8260 | 0.7532 | 0.8980 | 0.8115 | 0.9141 |
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+ | 0.0812 | 8.0963 | 3700 | 0.6640 | 0.8176 | 0.7284 | 0.9059 | 0.7989 | 0.9068 |
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+ | 0.1422 | 8.3151 | 3800 | 0.5916 | 0.8255 | 0.7721 | 0.8784 | 0.8149 | 0.9075 |
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+ | 0.1116 | 8.5339 | 3900 | 0.5746 | 0.8226 | 0.8583 | 0.7873 | 0.8279 | 0.9057 |
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+ | 0.1811 | 8.7527 | 4000 | 0.4679 | 0.8359 | 0.7869 | 0.8843 | 0.8267 | 0.9245 |
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+ | 0.1949 | 8.9716 | 4100 | 0.4645 | 0.8201 | 0.7641 | 0.8755 | 0.8086 | 0.9048 |
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+ | 0.036 | 9.1904 | 4200 | 0.6486 | 0.8349 | 0.7889 | 0.8804 | 0.8262 | 0.9116 |
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+ | 0.1117 | 9.4092 | 4300 | 0.5656 | 0.8236 | 0.7433 | 0.9029 | 0.8073 | 0.9125 |
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+ | 0.1101 | 9.6280 | 4400 | 0.5197 | 0.8285 | 0.8731 | 0.7843 | 0.8351 | 0.9226 |
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+ | 0.2064 | 9.8468 | 4500 | 0.6169 | 0.8270 | 0.7641 | 0.8892 | 0.8146 | 0.9132 |
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+ | 0.0647 | 10.0656 | 4600 | 0.5593 | 0.8255 | 0.7542 | 0.8961 | 0.8113 | 0.9122 |
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+ | 0.0566 | 10.2845 | 4700 | 0.5822 | 0.8245 | 0.7384 | 0.9098 | 0.8072 | 0.9182 |
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+ | 0.1324 | 10.5033 | 4800 | 0.5593 | 0.8319 | 0.7948 | 0.8686 | 0.8247 | 0.9146 |
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+ | 0.0824 | 10.7221 | 4900 | 0.6553 | 0.8117 | 0.7037 | 0.9186 | 0.7880 | 0.9130 |
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+ | 0.2134 | 10.9409 | 5000 | 0.5847 | 0.8334 | 0.8880 | 0.7794 | 0.8413 | 0.9271 |
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+ | 0.0835 | 11.1597 | 5100 | 0.6585 | 0.8314 | 0.8008 | 0.8618 | 0.8253 | 0.9130 |
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+ | 0.0936 | 11.3786 | 5200 | 0.8768 | 0.8191 | 0.7939 | 0.8441 | 0.8136 | 0.9062 |
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+ | 0.0325 | 11.5974 | 5300 | 0.6502 | 0.8423 | 0.8315 | 0.8529 | 0.8398 | 0.9209 |
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+ | 0.1054 | 11.8162 | 5400 | 0.5742 | 0.8354 | 0.8256 | 0.8451 | 0.833 | 0.9186 |
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+ | 0.0157 | 12.0350 | 5500 | 0.7790 | 0.8324 | 0.8256 | 0.8392 | 0.8305 | 0.9150 |
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+ | 0.0929 | 12.2538 | 5600 | 0.5779 | 0.8433 | 0.7978 | 0.8882 | 0.8351 | 0.9183 |
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+ | 0.0553 | 12.4726 | 5700 | 0.6642 | 0.8369 | 0.8157 | 0.8578 | 0.8326 | 0.9154 |
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+ | 0.1012 | 12.6915 | 5800 | 0.6882 | 0.8344 | 0.8494 | 0.8196 | 0.8361 | 0.9218 |
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+ | 0.1292 | 12.9103 | 5900 | 0.6949 | 0.8310 | 0.7800 | 0.8814 | 0.8211 | 0.9114 |
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+ | 0.103 | 13.1291 | 6000 | 0.7031 | 0.8398 | 0.8216 | 0.8578 | 0.8361 | 0.9162 |
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+ | 0.0652 | 13.3479 | 6100 | 0.7927 | 0.8379 | 0.8176 | 0.8578 | 0.8338 | 0.9178 |
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+ | 0.1194 | 13.5667 | 6200 | 0.7817 | 0.8211 | 0.7602 | 0.8814 | 0.8086 | 0.9125 |
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+ | 0.2684 | 13.7856 | 6300 | 0.7446 | 0.8221 | 0.7017 | 0.9412 | 0.7968 | 0.9164 |
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+ | 0.1194 | 14.0044 | 6400 | 0.7420 | 0.8334 | 0.8305 | 0.8363 | 0.8322 | 0.9152 |
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+ | 0.0548 | 14.2232 | 6500 | 0.8545 | 0.8295 | 0.8543 | 0.8049 | 0.8329 | 0.8918 |
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+ | 0.0681 | 14.4420 | 6600 | 0.8680 | 0.7915 | 0.6373 | 0.9441 | 0.7525 | 0.9094 |
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+ | 0.0627 | 14.6608 | 6700 | 0.6181 | 0.8487 | 0.8028 | 0.8941 | 0.8407 | 0.9206 |
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+ | 0.0565 | 14.8796 | 6800 | 0.7248 | 0.8241 | 0.8117 | 0.8363 | 0.8211 | 0.9108 |
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+ | 0.0879 | 15.0985 | 6900 | 0.6817 | 0.8295 | 0.8484 | 0.8108 | 0.8319 | 0.9208 |
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+ | 0.1235 | 15.3173 | 7000 | 0.7360 | 0.8344 | 0.8434 | 0.8255 | 0.8351 | 0.9143 |
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+ | 0.1256 | 15.5361 | 7100 | 0.6166 | 0.8300 | 0.7839 | 0.8755 | 0.8210 | 0.9114 |
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+ | 0.0353 | 15.7549 | 7200 | 0.7718 | 0.8339 | 0.8771 | 0.7912 | 0.8401 | 0.9231 |
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+ | 0.0838 | 15.9737 | 7300 | 0.7930 | 0.8305 | 0.7919 | 0.8686 | 0.8229 | 0.8963 |
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+ | 0.0345 | 16.1926 | 7400 | 0.9201 | 0.8231 | 0.7304 | 0.9147 | 0.8041 | 0.8816 |
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+ | 0.0263 | 16.4114 | 7500 | 0.8642 | 0.8310 | 0.7433 | 0.9176 | 0.8139 | 0.9021 |
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+ | 0.0471 | 16.6302 | 7600 | 0.8542 | 0.8324 | 0.7631 | 0.9010 | 0.8191 | 0.9031 |
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+ | 0.0894 | 16.8490 | 7700 | 0.7756 | 0.8034 | 0.7116 | 0.8941 | 0.7826 | 0.8999 |
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+ | 0.0649 | 17.0678 | 7800 | 0.7112 | 0.8344 | 0.8484 | 0.8206 | 0.8359 | 0.9077 |
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+ | 0.0567 | 17.2867 | 7900 | 0.7433 | 0.8452 | 0.8394 | 0.8510 | 0.8436 | 0.9106 |
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+ | 0.0229 | 17.5055 | 8000 | 0.8775 | 0.8255 | 0.7542 | 0.8961 | 0.8113 | 0.9017 |
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+ | 0.009 | 17.7243 | 8100 | 0.8561 | 0.8349 | 0.7958 | 0.8735 | 0.8274 | 0.9062 |
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+ | 0.0838 | 17.9431 | 8200 | 0.9441 | 0.8255 | 0.8771 | 0.7745 | 0.8333 | 0.9211 |
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+ | 0.0958 | 18.1619 | 8300 | 0.9286 | 0.8255 | 0.7374 | 0.9127 | 0.8078 | 0.8961 |
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+ | 0.0422 | 18.3807 | 8400 | 0.8053 | 0.8369 | 0.8186 | 0.8549 | 0.8331 | 0.9114 |
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+ | 0.053 | 18.5996 | 8500 | 0.8440 | 0.8388 | 0.8067 | 0.8706 | 0.8327 | 0.8972 |
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+ | 0.0462 | 18.8184 | 8600 | 0.7419 | 0.8221 | 0.8137 | 0.8304 | 0.8198 | 0.9108 |
159
+ | 0.0474 | 19.0372 | 8700 | 0.8702 | 0.8231 | 0.7849 | 0.8608 | 0.8152 | 0.8997 |
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+ | 0.0257 | 19.2560 | 8800 | 0.8966 | 0.8157 | 0.7473 | 0.8833 | 0.8013 | 0.9049 |
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+ | 0.0214 | 19.4748 | 8900 | 0.9787 | 0.8275 | 0.7839 | 0.8706 | 0.8188 | 0.8877 |
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+ | 0.1409 | 19.6937 | 9000 | 0.8695 | 0.8379 | 0.7899 | 0.8853 | 0.8289 | 0.9084 |
163
+ | 0.0715 | 19.9125 | 9100 | 0.9500 | 0.8245 | 0.8028 | 0.8461 | 0.8198 | 0.8975 |
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+ | 0.0331 | 20.1313 | 9200 | 0.9371 | 0.8334 | 0.8375 | 0.8294 | 0.8333 | 0.9042 |
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+ | 0.0259 | 20.3501 | 9300 | 0.8587 | 0.8374 | 0.8127 | 0.8618 | 0.8325 | 0.9124 |
166
+ | 0.0093 | 20.5689 | 9400 | 0.7861 | 0.8393 | 0.8196 | 0.8588 | 0.8354 | 0.9182 |
167
+ | 0.0103 | 20.7877 | 9500 | 0.7921 | 0.8359 | 0.7800 | 0.8912 | 0.8254 | 0.9119 |
168
+ | 0.1187 | 21.0066 | 9600 | 0.7618 | 0.8260 | 0.7512 | 0.9 | 0.8111 | 0.9166 |
169
+ | 0.0024 | 21.2254 | 9700 | 0.9334 | 0.8319 | 0.8632 | 0.8010 | 0.8363 | 0.9123 |
170
+ | 0.0993 | 21.4442 | 9800 | 0.8067 | 0.8310 | 0.8682 | 0.7941 | 0.8363 | 0.9177 |
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+ | 0.145 | 21.6630 | 9900 | 0.7816 | 0.8324 | 0.7770 | 0.8873 | 0.8218 | 0.9108 |
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+ | 0.054 | 21.8818 | 10000 | 0.8371 | 0.8413 | 0.8523 | 0.8304 | 0.8423 | 0.9190 |
173
+ | 0.0446 | 22.1007 | 10100 | 0.8001 | 0.8354 | 0.7899 | 0.8804 | 0.8268 | 0.9084 |
174
+ | 0.1218 | 22.3195 | 10200 | 0.8164 | 0.8364 | 0.7701 | 0.9020 | 0.8240 | 0.9078 |
175
+ | 0.032 | 22.5383 | 10300 | 0.8353 | 0.8359 | 0.8256 | 0.8461 | 0.8334 | 0.9157 |
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+ | 0.0804 | 22.7571 | 10400 | 0.8301 | 0.8314 | 0.7859 | 0.8765 | 0.8226 | 0.9149 |
177
+ | 0.0982 | 22.9759 | 10500 | 0.8366 | 0.8339 | 0.8305 | 0.8373 | 0.8326 | 0.9160 |
178
+ | 0.0153 | 23.1947 | 10600 | 0.8395 | 0.8295 | 0.7948 | 0.8637 | 0.8226 | 0.9150 |
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+ | 0.0647 | 23.4136 | 10700 | 0.8342 | 0.8364 | 0.8662 | 0.8069 | 0.8404 | 0.9230 |
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+ | 0.0906 | 23.6324 | 10800 | 0.8414 | 0.8078 | 0.8900 | 0.7265 | 0.8216 | 0.9166 |
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+ | 0.0071 | 23.8512 | 10900 | 0.8552 | 0.8354 | 0.7889 | 0.8814 | 0.8266 | 0.9053 |
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+ | 0.0254 | 24.0700 | 11000 | 0.8612 | 0.8428 | 0.7830 | 0.9020 | 0.8320 | 0.9009 |
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+ | 0.0265 | 24.2888 | 11100 | 1.0379 | 0.8245 | 0.7195 | 0.9284 | 0.8031 | 0.8937 |
184
+ | 0.048 | 24.5077 | 11200 | 1.0143 | 0.8285 | 0.7611 | 0.8951 | 0.8153 | 0.8942 |
185
+ | 0.0005 | 24.7265 | 11300 | 0.9883 | 0.8310 | 0.8077 | 0.8539 | 0.8262 | 0.9024 |
186
+ | 0.1702 | 24.9453 | 11400 | 1.0282 | 0.8339 | 0.7512 | 0.9157 | 0.8181 | 0.9078 |
187
+ | 0.0006 | 25.1641 | 11500 | 0.9612 | 0.8448 | 0.8712 | 0.8186 | 0.8480 | 0.9151 |
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+ | 0.0425 | 25.3829 | 11600 | 1.0040 | 0.8438 | 0.8612 | 0.8265 | 0.8457 | 0.9143 |
189
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+
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+
303
+ ### Framework versions
304
+
305
+ - Transformers 4.47.1
306
+ - Pytorch 2.5.1+cu121
307
+ - Datasets 2.20.0
308
+ - Tokenizers 0.21.0
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