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modernbert-tr-classifier

Text Classification
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modernbert
Generated from Trainer
text-embeddings-inference
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End of training

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  1. README.md +24 -12
  2. model.safetensors +1 -1
  3. runs/Jan23_02-58-33_ultramarine/events.out.tfevents.1737590313.ultramarine.3418180.0 +2 -2
README.md CHANGED
@@ -19,9 +19,9 @@ 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: 0.8455
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- - F1: 0.8730
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- - Accuracy: 0.8735
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  ## Model description
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@@ -46,20 +46,32 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 8
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
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- | 1.3023 | 1.0 | 146 | 1.0178 | 0.7250 | 0.7184 |
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- | 0.5327 | 2.0 | 292 | 0.5449 | 0.8415 | 0.8408 |
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- | 0.2336 | 3.0 | 438 | 0.5080 | 0.8685 | 0.8694 |
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- | 0.1116 | 4.0 | 584 | 0.6585 | 0.8489 | 0.8490 |
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- | 0.0399 | 5.0 | 730 | 1.0568 | 0.8415 | 0.8408 |
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- | 0.0137 | 6.0 | 876 | 0.9445 | 0.8526 | 0.8531 |
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- | 0.0074 | 7.0 | 1022 | 0.8898 | 0.8656 | 0.8653 |
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- | 0.0022 | 8.0 | 1168 | 0.8455 | 0.8730 | 0.8735 |
 
 
 
 
 
 
 
 
 
 
 
 
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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: 1.2880
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+ - F1: 0.8888
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+ - Accuracy: 0.8898
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  ## Model description
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.98) and epsilon=1e-06 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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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
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+ | 1.3439 | 1.0 | 146 | 0.7811 | 0.7564 | 0.7510 |
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+ | 0.5346 | 2.0 | 292 | 0.5727 | 0.8162 | 0.8163 |
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+ | 0.2959 | 3.0 | 438 | 0.5732 | 0.8061 | 0.8082 |
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+ | 0.1828 | 4.0 | 584 | 0.5832 | 0.8494 | 0.8490 |
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+ | 0.1113 | 5.0 | 730 | 0.8149 | 0.8564 | 0.8571 |
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+ | 0.0811 | 6.0 | 876 | 0.9747 | 0.8366 | 0.8367 |
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+ | 0.0427 | 7.0 | 1022 | 1.0021 | 0.8812 | 0.8816 |
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+ | 0.0493 | 8.0 | 1168 | 1.0971 | 0.8614 | 0.8612 |
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+ | 0.0494 | 9.0 | 1314 | 1.3082 | 0.8454 | 0.8449 |
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+ | 0.0205 | 10.0 | 1460 | 1.2811 | 0.8588 | 0.8571 |
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+ | 0.0079 | 11.0 | 1606 | 1.2820 | 0.8534 | 0.8531 |
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+ | 0.0214 | 12.0 | 1752 | 1.0864 | 0.8622 | 0.8612 |
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+ | 0.0113 | 13.0 | 1898 | 1.0554 | 0.8853 | 0.8857 |
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+ | 0.005 | 14.0 | 2044 | 1.3213 | 0.8784 | 0.8776 |
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+ | 0.0023 | 15.0 | 2190 | 1.2395 | 0.8734 | 0.8735 |
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+ | 0.0007 | 16.0 | 2336 | 1.3468 | 0.8870 | 0.8857 |
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+ | 0.0006 | 17.0 | 2482 | 1.3490 | 0.8813 | 0.8816 |
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+ | 0.0007 | 18.0 | 2628 | 1.2886 | 0.8888 | 0.8898 |
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+ | 0.0004 | 19.0 | 2774 | 1.2850 | 0.8888 | 0.8898 |
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+ | 0.0003 | 20.0 | 2920 | 1.2880 | 0.8888 | 0.8898 |
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  ### Framework versions
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