| --- |
| license: apache-2.0 |
| library_name: peft |
| tags: |
| - generated_from_trainer |
| base_model: mistralai/Mistral-7B-v0.1 |
| metrics: |
| - accuracy |
| model-index: |
| - name: Mistral-7B-v0.1_district-court-db |
| 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. --> |
|
|
| # Mistral-7B-v0.1_district-court-db |
| |
| This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0357 |
| - Precision Micro: 0.8047 |
| - Precision Macro: 0.6995 |
| - Recall Micro: 0.8047 |
| - Recall Macro: 0.6609 |
| - F1 Micro: 0.8047 |
| - F1 Macro: 0.6661 |
| - Accuracy: 0.8047 |
| |
| ## 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: 3e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: constant |
| - lr_scheduler_warmup_ratio: 0.03 |
| - training_steps: 725 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Macro | Recall Micro | Recall Macro | F1 Micro | F1 Macro | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------------:|:---------------:|:------------:|:------------:|:--------:|:--------:|:--------:| |
| | 0.0886 | 0.08 | 50 | 0.1082 | 0.5774 | 0.3988 | 0.5774 | 0.3124 | 0.5774 | 0.3222 | 0.5774 | |
| | 0.0572 | 0.16 | 100 | 0.0832 | 0.5877 | 0.4716 | 0.5877 | 0.3681 | 0.5877 | 0.3797 | 0.5877 | |
| | 0.0496 | 0.24 | 150 | 0.0525 | 0.7311 | 0.5911 | 0.7311 | 0.5747 | 0.7311 | 0.5703 | 0.7311 | |
| | 0.0541 | 0.32 | 200 | 0.0464 | 0.7566 | 0.6151 | 0.7566 | 0.5606 | 0.7566 | 0.5584 | 0.7566 | |
| | 0.0481 | 0.4 | 250 | 0.0433 | 0.7811 | 0.6636 | 0.7811 | 0.6514 | 0.7811 | 0.6369 | 0.7811 | |
| | 0.053 | 0.48 | 300 | 0.0452 | 0.7632 | 0.6936 | 0.7632 | 0.6461 | 0.7632 | 0.6338 | 0.7632 | |
| | 0.0401 | 0.56 | 350 | 0.0399 | 0.7943 | 0.7381 | 0.7943 | 0.6604 | 0.7943 | 0.6697 | 0.7943 | |
| | 0.0509 | 0.64 | 400 | 0.0393 | 0.8009 | 0.6546 | 0.8009 | 0.6612 | 0.8009 | 0.6501 | 0.8009 | |
| | 0.0474 | 0.72 | 450 | 0.0401 | 0.8019 | 0.7255 | 0.8019 | 0.6927 | 0.8019 | 0.6865 | 0.8019 | |
| | 0.045 | 0.79 | 500 | 0.0379 | 0.8009 | 0.7147 | 0.8009 | 0.7108 | 0.8009 | 0.6977 | 0.8009 | |
| | 0.0335 | 0.87 | 550 | 0.0369 | 0.8151 | 0.7046 | 0.8151 | 0.7335 | 0.8151 | 0.7135 | 0.8151 | |
| | 0.0429 | 0.95 | 600 | 0.0367 | 0.7962 | 0.7081 | 0.7962 | 0.6959 | 0.7962 | 0.6878 | 0.7962 | |
| | 0.0253 | 1.03 | 650 | 0.0342 | 0.8255 | 0.7370 | 0.8255 | 0.6975 | 0.8255 | 0.7098 | 0.8255 | |
| | 0.0311 | 1.11 | 700 | 0.0357 | 0.8047 | 0.6995 | 0.8047 | 0.6609 | 0.8047 | 0.6661 | 0.8047 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.7.1 |
| - Transformers 4.37.2 |
| - Pytorch 2.1.2+cu121 |
| - Datasets 2.17.1 |
| - Tokenizers 0.15.1 |
