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dnttestmee
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starclass_modernbert

Text Classification
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modernbert
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  1. README.md +11 -11
  2. logs/events.out.tfevents.1737587216.PC +3 -0
  3. model.safetensors +1 -1
  4. training_args.bin +1 -1
README.md CHANGED
@@ -21,11 +21,11 @@ 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 the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1793
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- - Accuracy: 0.9048
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- - Precision: 0.9140
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- - Recall: 0.9048
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- - F1: 0.9037
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  ## Model description
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@@ -49,18 +49,18 @@ The following hyperparameters were used during training:
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  - eval_batch_size: 16
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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: cosine
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  - num_epochs: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 1.2429 | 1.0 | 16 | 0.4839 | 0.8095 | 0.8631 | 0.8095 | 0.8122 |
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- | 0.2833 | 2.0 | 32 | 0.1735 | 0.9365 | 0.9374 | 0.9365 | 0.9365 |
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- | 0.0735 | 3.0 | 48 | 0.1645 | 0.9365 | 0.9364 | 0.9365 | 0.9360 |
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- | 0.0038 | 4.0 | 64 | 0.1804 | 0.9048 | 0.9147 | 0.9048 | 0.9055 |
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- | 0.0009 | 5.0 | 80 | 0.1793 | 0.9048 | 0.9140 | 0.9048 | 0.9037 |
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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 the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2026
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+ - Accuracy: 0.9524
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+ - Precision: 0.9545
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+ - Recall: 0.9524
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+ - F1: 0.9529
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  ## Model description
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  - eval_batch_size: 16
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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: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 1.0385 | 1.0 | 16 | 0.2928 | 0.9048 | 0.9188 | 0.9048 | 0.9057 |
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+ | 0.1931 | 2.0 | 32 | 0.1816 | 0.9206 | 0.9284 | 0.9206 | 0.9222 |
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+ | 0.0984 | 3.0 | 48 | 0.2056 | 0.9365 | 0.9384 | 0.9365 | 0.9370 |
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+ | 0.0027 | 4.0 | 64 | 0.1988 | 0.9524 | 0.9545 | 0.9524 | 0.9529 |
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+ | 0.0003 | 5.0 | 80 | 0.2026 | 0.9524 | 0.9545 | 0.9524 | 0.9529 |
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  ### Framework versions
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