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Mistral-7B-v0.1_caselaw

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Mistral-7B-v0.1_caselaw / README.md
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metadata
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: []

Mistral-7B-v0.1_district-court-db

This model is a fine-tuned version of 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