| --- |
| tags: |
| - generated_from_trainer |
| base_model: openaccess-ai-collective/tiny-mistral |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| model-index: |
| - name: tiny-mistral |
| results: [] |
| --- |
| ## Metrics Upon Eval with max_length = 512 |
| |
| - loss: 2.4489 |
| - accuracy: 0.7250 |
| - precision: 0.7150 |
| - recall: 0.7250 |
| - precision_macro: 0.6583 |
| - recall_macro: 0.6262 |
| - macro_fpr: 0.0278 |
| - weighted_fpr: 0.0264 |
| - weighted_specificity: 0.9597 |
| - macro_specificity: 0.9790 |
| - weighted_sensitivity: 0.7250 |
| - macro_sensitivity: 0.6262 |
| - f1_micro: 0.7250 |
| - f1_macro: 0.6317 |
| - f1_weighted: 0.7155 |
| - runtime: 27.7396 |
| - samples_per_second: 46.5400 |
| - steps_per_second: 5.8400 |
|
|
|
|
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # tiny-mistral |
|
|
| This model is a fine-tuned version of [openaccess-ai-collective/tiny-mistral](https://huggingface.co/openaccess-ai-collective/tiny-mistral) on an unknown dataset. |
| It achieves the following results on the evaluation set (at last epoch): |
| - Loss: 2.5607 |
| - Accuracy: 0.7126 |
| - Precision: 0.7033 |
| - Recall: 0.7126 |
| - Precision Macro: 0.6443 |
| - Recall Macro: 0.5942 |
| - Macro Fpr: 0.0292 |
| - Weighted Fpr: 0.0282 |
| - Weighted Specificity: 0.9577 |
| - Macro Specificity: 0.9779 |
| - Weighted Sensitivity: 0.7111 |
| - Macro Sensitivity: 0.5942 |
| - F1 Micro: 0.7111 |
| - F1 Macro: 0.6107 |
| - F1 Weighted: 0.7086 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:| |
| | 1.4479 | 1.0 | 643 | 1.1182 | 0.6499 | 0.6258 | 0.6499 | 0.4712 | 0.4744 | 0.0390 | 0.0371 | 0.9470 | 0.9731 | 0.6499 | 0.4744 | 0.6499 | 0.4547 | 0.6214 | |
| | 0.8133 | 2.0 | 1286 | 1.0854 | 0.6987 | 0.7197 | 0.6987 | 0.5877 | 0.5528 | 0.0305 | 0.0299 | 0.9608 | 0.9773 | 0.6987 | 0.5528 | 0.6987 | 0.5474 | 0.6970 | |
| | 0.5592 | 3.0 | 1929 | 1.6114 | 0.6987 | 0.7107 | 0.6987 | 0.6368 | 0.5881 | 0.0304 | 0.0299 | 0.9609 | 0.9773 | 0.6987 | 0.5881 | 0.6987 | 0.6013 | 0.6998 | |
| | 0.2375 | 4.0 | 2572 | 1.7779 | 0.6956 | 0.7001 | 0.6956 | 0.5840 | 0.5667 | 0.0310 | 0.0303 | 0.9566 | 0.9768 | 0.6956 | 0.5667 | 0.6956 | 0.5699 | 0.6923 | |
| | 0.1586 | 5.0 | 3215 | 2.1752 | 0.6948 | 0.7011 | 0.6948 | 0.5797 | 0.5799 | 0.0316 | 0.0304 | 0.9601 | 0.9770 | 0.6948 | 0.5799 | 0.6948 | 0.5695 | 0.6917 | |
| | 0.0956 | 6.0 | 3858 | 2.3261 | 0.7080 | 0.7213 | 0.7080 | 0.6169 | 0.6191 | 0.0291 | 0.0286 | 0.9646 | 0.9782 | 0.7080 | 0.6191 | 0.7080 | 0.6115 | 0.7105 | |
| | 0.044 | 7.0 | 4501 | 2.3308 | 0.7157 | 0.7143 | 0.7157 | 0.6184 | 0.5939 | 0.0285 | 0.0276 | 0.9611 | 0.9785 | 0.7157 | 0.5939 | 0.7157 | 0.6014 | 0.7131 | |
| | 0.0212 | 8.0 | 5144 | 2.5607 | 0.7126 | 0.7033 | 0.7126 | 0.6494 | 0.6175 | 0.0294 | 0.0280 | 0.9581 | 0.9780 | 0.7126 | 0.6175 | 0.7126 | 0.6237 | 0.7047 | |
| | 0.0183 | 9.0 | 5787 | 2.6405 | 0.7119 | 0.7092 | 0.7119 | 0.6133 | 0.5850 | 0.0291 | 0.0281 | 0.9599 | 0.9781 | 0.7119 | 0.5850 | 0.7119 | 0.5935 | 0.7088 | |
| | 0.0145 | 10.0 | 6430 | 2.7268 | 0.7088 | 0.7058 | 0.7088 | 0.6235 | 0.5945 | 0.0297 | 0.0285 | 0.9574 | 0.9777 | 0.7088 | 0.5945 | 0.7088 | 0.6039 | 0.7051 | |
| | 0.0065 | 11.0 | 7073 | 2.7568 | 0.7149 | 0.7133 | 0.7149 | 0.6342 | 0.5966 | 0.0286 | 0.0277 | 0.9609 | 0.9784 | 0.7149 | 0.5966 | 0.7149 | 0.6068 | 0.7123 | |
| | 0.0012 | 12.0 | 7716 | 2.9243 | 0.7088 | 0.7106 | 0.7088 | 0.6261 | 0.5886 | 0.0296 | 0.0285 | 0.9581 | 0.9778 | 0.7088 | 0.5886 | 0.7088 | 0.6011 | 0.7071 | |
| | 0.0019 | 13.0 | 8359 | 2.9101 | 0.7119 | 0.7107 | 0.7119 | 0.6399 | 0.5910 | 0.0291 | 0.0281 | 0.9576 | 0.9780 | 0.7119 | 0.5910 | 0.7119 | 0.6073 | 0.7085 | |
| | 0.0011 | 14.0 | 9002 | 2.9270 | 0.7103 | 0.7101 | 0.7103 | 0.6430 | 0.5925 | 0.0293 | 0.0283 | 0.9576 | 0.9779 | 0.7103 | 0.5925 | 0.7103 | 0.6090 | 0.7077 | |
| | 0.0008 | 15.0 | 9645 | 2.9390 | 0.7111 | 0.7110 | 0.7111 | 0.6443 | 0.5942 | 0.0292 | 0.0282 | 0.9577 | 0.9779 | 0.7111 | 0.5942 | 0.7111 | 0.6107 | 0.7086 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.39.3 |
| - Pytorch 2.1.2 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
|
|
