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---
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: 0.0358
- Precision Micro: 0.8142
- Precision Macro: 0.7222
- Recall Micro: 0.8142
- Recall Macro: 0.7126
- F1 Micro: 0.8142
- F1 Macro: 0.7098
- Accuracy: 0.8142
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 1450
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Macro | Recall Micro | Recall Macro | F1 Micro | F1 Macro | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:---------------:|:------------:|:------------:|:--------:|:--------:|:--------:|
| 0.1255 | 0.04 | 50 | 0.2459 | 0.2330 | 0.0980 | 0.2330 | 0.0939 | 0.2330 | 0.0773 | 0.2330 |
| 0.1076 | 0.08 | 100 | 0.1451 | 0.4075 | 0.1951 | 0.4075 | 0.1846 | 0.4075 | 0.1681 | 0.4075 |
| 0.066 | 0.12 | 150 | 0.1095 | 0.5387 | 0.3493 | 0.5387 | 0.2872 | 0.5387 | 0.2780 | 0.5387 |
| 0.0699 | 0.16 | 200 | 0.0901 | 0.6208 | 0.3837 | 0.6208 | 0.3992 | 0.6208 | 0.3798 | 0.6208 |
| 0.066 | 0.2 | 250 | 0.0883 | 0.6104 | 0.4544 | 0.6104 | 0.4312 | 0.6104 | 0.4135 | 0.6104 |
| 0.0452 | 0.24 | 300 | 0.0879 | 0.6877 | 0.5649 | 0.6877 | 0.5135 | 0.6877 | 0.5092 | 0.6877 |
| 0.0545 | 0.28 | 350 | 0.0761 | 0.6764 | 0.5194 | 0.6764 | 0.5288 | 0.6764 | 0.5040 | 0.6764 |
| 0.0647 | 0.32 | 400 | 0.0665 | 0.7340 | 0.6193 | 0.7340 | 0.5252 | 0.7340 | 0.5493 | 0.7340 |
| 0.056 | 0.36 | 450 | 0.0514 | 0.7396 | 0.6097 | 0.7396 | 0.5767 | 0.7396 | 0.5672 | 0.7396 |
| 0.0513 | 0.4 | 500 | 0.0479 | 0.7613 | 0.6384 | 0.7613 | 0.6145 | 0.7613 | 0.6020 | 0.7613 |
| 0.0501 | 0.44 | 550 | 0.0502 | 0.7509 | 0.6245 | 0.7509 | 0.6167 | 0.7509 | 0.6075 | 0.7509 |
| 0.0533 | 0.48 | 600 | 0.0481 | 0.7642 | 0.6500 | 0.7642 | 0.6139 | 0.7642 | 0.6073 | 0.7642 |
| 0.0462 | 0.52 | 650 | 0.0473 | 0.7481 | 0.5942 | 0.7481 | 0.5740 | 0.7481 | 0.5679 | 0.7481 |
| 0.0496 | 0.56 | 700 | 0.0419 | 0.7972 | 0.6678 | 0.7972 | 0.6480 | 0.7972 | 0.6518 | 0.7972 |
| 0.0614 | 0.6 | 750 | 0.0489 | 0.7774 | 0.6678 | 0.7774 | 0.6360 | 0.7774 | 0.6308 | 0.7774 |
| 0.0468 | 0.64 | 800 | 0.0443 | 0.7830 | 0.6435 | 0.7830 | 0.6816 | 0.7830 | 0.6494 | 0.7830 |
| 0.0477 | 0.68 | 850 | 0.0420 | 0.7972 | 0.7040 | 0.7972 | 0.6567 | 0.7972 | 0.6663 | 0.7972 |
| 0.0519 | 0.72 | 900 | 0.0463 | 0.7632 | 0.6519 | 0.7632 | 0.6291 | 0.7632 | 0.6292 | 0.7632 |
| 0.0453 | 0.76 | 950 | 0.0429 | 0.7802 | 0.6757 | 0.7802 | 0.6698 | 0.7802 | 0.6564 | 0.7802 |
| 0.0452 | 0.79 | 1000 | 0.0471 | 0.7377 | 0.6182 | 0.7377 | 0.6300 | 0.7377 | 0.6049 | 0.7377 |
| 0.0367 | 0.83 | 1050 | 0.0388 | 0.7981 | 0.6857 | 0.7981 | 0.6992 | 0.7981 | 0.6801 | 0.7981 |
| 0.0377 | 0.87 | 1100 | 0.0382 | 0.8 | 0.6636 | 0.8 | 0.6698 | 0.8000 | 0.6591 | 0.8 |
| 0.0429 | 0.91 | 1150 | 0.0398 | 0.7953 | 0.6924 | 0.7953 | 0.6441 | 0.7953 | 0.6466 | 0.7953 |
| 0.0451 | 0.95 | 1200 | 0.0378 | 0.7943 | 0.6713 | 0.7943 | 0.6538 | 0.7943 | 0.6535 | 0.7943 |
| 0.0347 | 0.99 | 1250 | 0.0413 | 0.7840 | 0.6735 | 0.7840 | 0.6450 | 0.7840 | 0.6331 | 0.7840 |
| 0.0378 | 1.03 | 1300 | 0.0377 | 0.8047 | 0.7109 | 0.8047 | 0.6387 | 0.8047 | 0.6489 | 0.8047 |
| 0.0357 | 1.07 | 1350 | 0.0386 | 0.8028 | 0.6899 | 0.8028 | 0.6559 | 0.8028 | 0.6649 | 0.8028 |
| 0.0418 | 1.11 | 1400 | 0.0368 | 0.7962 | 0.7114 | 0.7962 | 0.6942 | 0.7962 | 0.6910 | 0.7962 |
| 0.0293 | 1.15 | 1450 | 0.0358 | 0.8142 | 0.7222 | 0.8142 | 0.7126 | 0.8142 | 0.7098 | 0.8142 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.17.1
- Tokenizers 0.15.1 |