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license: apache-2.0 |
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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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- precision |
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- recall |
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
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model-index: |
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- name: tiny-llama |
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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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# tiny-llama |
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This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co./TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6966 |
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- Accuracy: 0.8195 |
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- Precision: 0.8222 |
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- Recall: 0.8195 |
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- Precision Macro: 0.7955 |
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- Recall Macro: 0.7536 |
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- Macro Fpr: 0.0148 |
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- Weighted Fpr: 0.0141 |
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- Weighted Specificity: 0.9765 |
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- Macro Specificity: 0.9873 |
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- Weighted Sensitivity: 0.8327 |
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- Macro Sensitivity: 0.7536 |
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- F1 Micro: 0.8327 |
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- F1 Macro: 0.7609 |
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- F1 Weighted: 0.8291 |
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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: 5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:| |
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| 1.0444 | 1.0 | 642 | 0.5968 | 0.8056 | 0.8050 | 0.8056 | 0.7122 | 0.6995 | 0.0175 | 0.0169 | 0.9730 | 0.9852 | 0.8056 | 0.6995 | 0.8056 | 0.6986 | 0.8014 | |
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| 0.4788 | 2.0 | 1284 | 0.6966 | 0.8195 | 0.8222 | 0.8195 | 0.8092 | 0.7825 | 0.0161 | 0.0155 | 0.9755 | 0.9863 | 0.8195 | 0.7825 | 0.8195 | 0.7849 | 0.8172 | |
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| 0.3354 | 3.0 | 1926 | 0.8046 | 0.8327 | 0.8276 | 0.8327 | 0.8058 | 0.7582 | 0.0148 | 0.0141 | 0.9758 | 0.9872 | 0.8327 | 0.7582 | 0.8327 | 0.7742 | 0.8282 | |
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| 0.0571 | 4.0 | 2569 | 1.1143 | 0.8265 | 0.8312 | 0.8265 | 0.7904 | 0.7763 | 0.0152 | 0.0148 | 0.9772 | 0.9869 | 0.8265 | 0.7763 | 0.8265 | 0.7690 | 0.8262 | |
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| 0.0187 | 5.0 | 3211 | 1.1104 | 0.8319 | 0.8316 | 0.8319 | 0.7745 | 0.7724 | 0.0149 | 0.0142 | 0.9770 | 0.9873 | 0.8319 | 0.7724 | 0.8319 | 0.7638 | 0.8303 | |
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| 0.0071 | 6.0 | 3853 | 1.1445 | 0.8242 | 0.8210 | 0.8242 | 0.7684 | 0.7384 | 0.0157 | 0.0150 | 0.9755 | 0.9866 | 0.8242 | 0.7384 | 0.8242 | 0.7451 | 0.8209 | |
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| 0.0002 | 7.0 | 4495 | 1.2032 | 0.8327 | 0.8302 | 0.8327 | 0.7985 | 0.7529 | 0.0148 | 0.0141 | 0.9765 | 0.9873 | 0.8327 | 0.7529 | 0.8327 | 0.7617 | 0.8293 | |
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| 0.0028 | 8.0 | 5138 | 1.1918 | 0.8257 | 0.8226 | 0.8257 | 0.7738 | 0.7493 | 0.0155 | 0.0149 | 0.9756 | 0.9868 | 0.8257 | 0.7493 | 0.8257 | 0.7552 | 0.8229 | |
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| 0.0 | 9.0 | 5780 | 1.2181 | 0.8311 | 0.8286 | 0.8311 | 0.7935 | 0.7522 | 0.0150 | 0.0143 | 0.9764 | 0.9872 | 0.8311 | 0.7522 | 0.8311 | 0.7592 | 0.8276 | |
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| 0.0018 | 10.0 | 6420 | 1.2265 | 0.8327 | 0.8301 | 0.8327 | 0.7955 | 0.7536 | 0.0148 | 0.0141 | 0.9765 | 0.9873 | 0.8327 | 0.7536 | 0.8327 | 0.7609 | 0.8291 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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