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staghado
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edu-modernbert

Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
Inference Endpoints
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  1. README.md +19 -15
  2. final/model.safetensors +1 -1
  3. final/training_args.bin +1 -1
  4. model.safetensors +1 -1
README.md CHANGED
@@ -21,11 +21,15 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2863
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- - Precision: 0.5402
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- - Recall: 0.3945
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  - F1: 0.4305
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- - Accuracy: 0.6822
 
 
 
 
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  ## Model description
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@@ -54,17 +58,17 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0 | 0 | 1.7219 | 0.1827 | 0.1714 | 0.0934 | 0.2345 |
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- | 0.3195 | 2.4331 | 1000 | 0.3180 | 0.5267 | 0.3632 | 0.3841 | 0.6562 |
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- | 0.3096 | 4.8662 | 2000 | 0.3028 | 0.5275 | 0.3827 | 0.4108 | 0.6652 |
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- | 0.3027 | 7.2993 | 3000 | 0.2985 | 0.5332 | 0.3905 | 0.4223 | 0.6681 |
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- | 0.3004 | 9.7324 | 4000 | 0.2919 | 0.5392 | 0.3867 | 0.4204 | 0.6774 |
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- | 0.2965 | 12.1655 | 5000 | 0.2896 | 0.5345 | 0.3970 | 0.4311 | 0.6788 |
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- | 0.2942 | 14.5985 | 6000 | 0.2885 | 0.5355 | 0.3960 | 0.4312 | 0.6819 |
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- | 0.287 | 17.0316 | 7000 | 0.2912 | 0.5360 | 0.3813 | 0.4170 | 0.6828 |
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- | 0.2893 | 19.4647 | 8000 | 0.2863 | 0.5402 | 0.3945 | 0.4305 | 0.6822 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2864
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+ - Precision: 0.5389
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+ - Recall: 0.3949
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  - F1: 0.4305
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+ - Accuracy: 0.6820
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+ - Binary Precision: 0.7559
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+ - Binary Recall: 0.4496
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+ - Binary F1: 0.5638
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+ - Binary Accuracy: 0.9373
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Binary Precision | Binary Recall | Binary F1 | Binary Accuracy |
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+ |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:----------------:|:-------------:|:---------:|:---------------:|
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+ | No log | 0 | 0 | 2.4591 | 0.1116 | 0.1645 | 0.0484 | 0.1386 | 0.0 | 0.0 | 0.0 | 0.9098 |
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+ | 0.3196 | 2.4331 | 1000 | 0.3097 | 0.5201 | 0.3691 | 0.3946 | 0.6587 | 0.7614 | 0.3580 | 0.4870 | 0.9320 |
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+ | 0.3064 | 4.8662 | 2000 | 0.3067 | 0.5273 | 0.3882 | 0.4154 | 0.6599 | 0.7391 | 0.4375 | 0.5496 | 0.9353 |
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+ | 0.3088 | 7.2993 | 3000 | 0.2951 | 0.5353 | 0.3833 | 0.4169 | 0.6744 | 0.7656 | 0.4007 | 0.5261 | 0.9349 |
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+ | 0.2991 | 9.7324 | 4000 | 0.2975 | 0.5421 | 0.3921 | 0.4234 | 0.6699 | 0.7316 | 0.4643 | 0.5681 | 0.9363 |
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+ | 0.2957 | 12.1655 | 5000 | 0.2920 | 0.5362 | 0.3859 | 0.4207 | 0.6813 | 0.7811 | 0.3953 | 0.5249 | 0.9355 |
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+ | 0.2932 | 14.5985 | 6000 | 0.2881 | 0.5364 | 0.3946 | 0.4298 | 0.6824 | 0.7591 | 0.4351 | 0.5532 | 0.9366 |
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+ | 0.2862 | 17.0316 | 7000 | 0.2876 | 0.5411 | 0.3850 | 0.4213 | 0.6829 | 0.7713 | 0.4104 | 0.5358 | 0.9359 |
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+ | 0.2894 | 19.4647 | 8000 | 0.2864 | 0.5389 | 0.3949 | 0.4305 | 0.6820 | 0.7559 | 0.4496 | 0.5638 | 0.9373 |
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  ### Framework versions
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