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---
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 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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