---
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
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# 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