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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-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: kpi-priority-model |
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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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# kpi-priority-model |
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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: 0.7414 |
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- Accuracy: 0.7288 |
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- F1: 0.7266 |
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- Classification Report: precision recall f1-score support |
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Low 0.68 0.83 0.75 94 |
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Medium 0.66 0.52 0.58 111 |
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High 0.69 0.73 0.71 202 |
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Critical 0.81 0.78 0.80 253 |
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accuracy 0.73 660 |
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macro avg 0.71 0.72 0.71 660 |
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weighted avg 0.73 0.73 0.73 660 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Classification Report | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 2.3852 | 1.0 | 165 | 0.8176 | 0.6833 | 0.6838 | precision recall f1-score support |
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Low 0.65 0.85 0.74 94 |
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Medium 0.55 0.55 0.55 111 |
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High 0.62 0.78 0.69 202 |
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Critical 0.88 0.60 0.72 253 |
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accuracy 0.68 660 |
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macro avg 0.68 0.70 0.67 660 |
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weighted avg 0.71 0.68 0.68 660 |
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| 1.3946 | 2.0 | 330 | 0.7414 | 0.7288 | 0.7266 | precision recall f1-score support |
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Low 0.68 0.83 0.75 94 |
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Medium 0.66 0.52 0.58 111 |
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High 0.69 0.73 0.71 202 |
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Critical 0.81 0.78 0.80 253 |
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accuracy 0.73 660 |
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macro avg 0.71 0.72 0.71 660 |
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weighted avg 0.73 0.73 0.73 660 |
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### Framework versions |
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- Transformers 4.49.0.dev0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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