| | --- |
| | license: apache-2.0 |
| | library_name: peft |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - accuracy |
| | base_model: mistralai/Mistral-7B-v0.1 |
| | model-index: |
| | - name: one_half_data_high_rank_v2 |
| | results: [] |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # one_half_data_high_rank_v2 |
| | |
| | This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.6564 |
| | - Precision: 0.8403 |
| | - Recall: 0.9383 |
| | - F1-score: 0.8866 |
| | - Accuracy: 0.8450 |
| | |
| | ## 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: 0.0001 |
| | - 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: constant |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 1 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy | |
| | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:| |
| | | 0.535 | 1.0 | 24544 | 0.6564 | 0.8403 | 0.9383 | 0.8866 | 0.8450 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - PEFT 0.9.0 |
| | - Transformers 4.38.2 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.15.2 |
