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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: answerdotai/ModernBERT-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: modernbert-disfluency-optimized |
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results: [] |
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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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# modernbert-disfluency-optimized |
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This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0126 |
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- Precision: 0.0827 |
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- Recall: 0.4119 |
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- F1: 0.1378 |
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- Accuracy: 0.2107 |
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- Artial Word F1: 0.0 |
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- Artial Word Precision: 0.0 |
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- Artial Word Recall: 0.0 |
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- Ause F1: 0.6721 |
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- Ause Precision: 0.5256 |
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- Ause Recall: 0.9318 |
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- Epetition F1: 0.0548 |
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- Epetition Precision: 0.0350 |
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- Epetition Recall: 0.1270 |
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- Evision F1: 0.0079 |
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- Evision Precision: 0.0042 |
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- Evision Recall: 0.0833 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 24 |
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- eval_batch_size: 48 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 48 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 15 |
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- mixed_precision_training: Native AMP |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Artial Word F1 | Artial Word Precision | Artial Word Recall | Ause F1 | Ause Precision | Ause Recall | Epetition F1 | Epetition Precision | Epetition Recall | Evision F1 | Evision Precision | Evision Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:--------------:|:---------------------:|:------------------:|:-------:|:--------------:|:-----------:|:------------:|:-------------------:|:----------------:|:----------:|:-----------------:|:--------------:| |
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| 0.0513 | 1.0 | 58 | 0.0229 | 0.0192 | 0.1360 | 0.0336 | 0.1402 | 0.0 | 0.0 | 0.0 | 0.0731 | 0.0422 | 0.2721 | 0.0223 | 0.0136 | 0.0619 | 0.0046 | 0.0025 | 0.0357 | |
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| 0.0524 | 2.0 | 116 | 0.0174 | 0.0391 | 0.2594 | 0.0680 | 0.1631 | 0.0 | 0.0 | 0.0 | 0.1960 | 0.1172 | 0.5986 | 0.0229 | 0.0138 | 0.0670 | 0.0041 | 0.0022 | 0.0357 | |
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| 0.0463 | 3.0 | 174 | 0.0146 | 0.0501 | 0.3048 | 0.0861 | 0.1647 | 0.0 | 0.0 | 0.0 | 0.3093 | 0.1974 | 0.7143 | 0.0245 | 0.0150 | 0.0670 | 0.0057 | 0.0030 | 0.0536 | |
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| 0.0166 | 4.0 | 232 | 0.0129 | 0.0593 | 0.3401 | 0.1010 | 0.1874 | 0.0 | 0.0 | 0.0 | 0.4041 | 0.2708 | 0.7959 | 0.0286 | 0.0176 | 0.0773 | 0.0058 | 0.0030 | 0.0536 | |
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| 0.0707 | 5.0 | 290 | 0.0121 | 0.0651 | 0.3652 | 0.1106 | 0.1938 | 0.0 | 0.0 | 0.0 | 0.5050 | 0.3580 | 0.8571 | 0.0302 | 0.0185 | 0.0825 | 0.0057 | 0.0030 | 0.0536 | |
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| 0.013 | 6.0 | 348 | 0.0115 | 0.0726 | 0.3829 | 0.1221 | 0.2020 | 0.0 | 0.0 | 0.0 | 0.5586 | 0.4068 | 0.8912 | 0.0368 | 0.0230 | 0.0928 | 0.0058 | 0.0030 | 0.0536 | |
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| 0.032 | 7.0 | 406 | 0.0112 | 0.0788 | 0.4055 | 0.1320 | 0.1907 | 0.0 | 0.0 | 0.0 | 0.6279 | 0.4770 | 0.9184 | 0.0434 | 0.0272 | 0.1082 | 0.0096 | 0.0051 | 0.0893 | |
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| 0.0293 | 8.0 | 464 | 0.0109 | 0.0789 | 0.4081 | 0.1322 | 0.2038 | 0.0 | 0.0 | 0.0 | 0.6492 | 0.5 | 0.9252 | 0.0463 | 0.0288 | 0.1186 | 0.0058 | 0.0031 | 0.0536 | |
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| 0.0267 | 9.0 | 522 | 0.0107 | 0.0785 | 0.4055 | 0.1316 | 0.2049 | 0.0 | 0.0 | 0.0 | 0.6667 | 0.5211 | 0.9252 | 0.0423 | 0.0263 | 0.1082 | 0.0077 | 0.0041 | 0.0714 | |
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| 0.0244 | 10.0 | 580 | 0.0106 | 0.0801 | 0.4106 | 0.1340 | 0.2026 | 0.0 | 0.0 | 0.0 | 0.685 | 0.5415 | 0.9320 | 0.0448 | 0.0279 | 0.1134 | 0.0076 | 0.0040 | 0.0714 | |
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| 0.0104 | 11.0 | 638 | 0.0105 | 0.0818 | 0.4131 | 0.1366 | 0.2080 | 0.0 | 0.0 | 0.0 | 0.6954 | 0.5547 | 0.9320 | 0.0477 | 0.0298 | 0.1186 | 0.0077 | 0.0041 | 0.0714 | |
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| 0.0352 | 12.0 | 696 | 0.0104 | 0.0836 | 0.4156 | 0.1392 | 0.2051 | 0.0 | 0.0 | 0.0 | 0.7023 | 0.5610 | 0.9388 | 0.0487 | 0.0306 | 0.1186 | 0.0078 | 0.0041 | 0.0714 | |
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| 0.0216 | 13.0 | 754 | 0.0104 | 0.0827 | 0.4106 | 0.1376 | 0.2032 | 0.0 | 0.0 | 0.0 | 0.7095 | 0.5702 | 0.9388 | 0.0443 | 0.0279 | 0.1082 | 0.0078 | 0.0041 | 0.0714 | |
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| 0.0211 | 14.0 | 812 | 0.0104 | 0.0828 | 0.4106 | 0.1378 | 0.2028 | 0.0 | 0.0 | 0.0 | 0.7095 | 0.5702 | 0.9388 | 0.0443 | 0.0279 | 0.1082 | 0.0078 | 0.0041 | 0.0714 | |
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| 0.0208 | 15.0 | 870 | 0.0104 | 0.0828 | 0.4106 | 0.1378 | 0.2030 | 0.0 | 0.0 | 0.0 | 0.7095 | 0.5702 | 0.9388 | 0.0444 | 0.0279 | 0.1082 | 0.0078 | 0.0041 | 0.0714 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.4.0 |
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- Tokenizers 0.21.0 |
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