metadata
library_name: transformers
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
tags:
- generated_from_trainer
model-index:
- name: mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify
results: []
mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 on the None dataset. It achieves the following results on the evaluation set:
- F1 Micro: 0.0
- F1 Macro: 0.0
- Precision At 5: 0.1676
- Recall At 5: 0.0597
- Precision At 8: 0.1379
- Recall At 8: 0.0770
- Precision At 15: 0.0926
- Recall At 15: 0.0992
- Rare F1 Micro: 0.0
- Rare F1 Macro: 0.0
- Rare Precision: 0.0
- Rare Recall: 0.0
- Rare Precision At 5: 0.1279
- Rare Recall At 5: 0.0462
- Rare Precision At 8: 0.0938
- Rare Recall At 8: 0.0522
- Rare Precision At 15: 0.0642
- Rare Recall At 15: 0.0700
- Not Rare F1 Micro: 0.5956
- Not Rare F1 Macro: 0.3733
- Not Rare Precision: 0.5956
- Not Rare Recall: 0.5956
- Not Rare Precision At 5: 0.0809
- Not Rare Recall At 5: 0.4044
- Not Rare Precision At 8: 0.0506
- Not Rare Recall At 8: 0.4044
- Not Rare Precision At 15: 0.0270
- Not Rare Recall At 15: 0.4044
- Loss: 0.1050
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | F1 Micro | F1 Macro | Precision At 5 | Recall At 5 | Precision At 8 | Recall At 8 | Precision At 15 | Recall At 15 | Rare F1 Micro | Rare F1 Macro | Rare Precision | Rare Recall | Rare Precision At 5 | Rare Recall At 5 | Rare Precision At 8 | Rare Recall At 8 | Rare Precision At 15 | Rare Recall At 15 | Not Rare F1 Micro | Not Rare F1 Macro | Not Rare Precision | Not Rare Recall | Not Rare Precision At 5 | Not Rare Recall At 5 | Not Rare Precision At 8 | Not Rare Recall At 8 | Not Rare Precision At 15 | Not Rare Recall At 15 | Validation Loss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.624 | 1.0 | 18 | 0.0 | 0.0 | 0.0059 | 0.0013 | 0.0110 | 0.0037 | 0.0147 | 0.0111 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0103 | 0.0024 | 0.0129 | 0.0059 | 0.0142 | 0.0114 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.2217 |
| 0.1249 | 2.0 | 36 | 0.0 | 0.0 | 0.0088 | 0.0018 | 0.0110 | 0.0038 | 0.0152 | 0.0110 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0088 | 0.0018 | 0.0110 | 0.0047 | 0.0142 | 0.0124 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1219 |
| 0.1086 | 3.0 | 54 | 0.0 | 0.0 | 0.0471 | 0.0121 | 0.0506 | 0.0238 | 0.0456 | 0.0457 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0368 | 0.0106 | 0.0414 | 0.0198 | 0.0368 | 0.0370 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1074 |
| 0.1033 | 3.7887 | 68 | 0.0 | 0.0 | 0.1676 | 0.0597 | 0.1379 | 0.0770 | 0.0926 | 0.0992 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1279 | 0.0462 | 0.0938 | 0.0522 | 0.0642 | 0.0700 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1050 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0
- Datasets 3.6.0
- Tokenizers 0.21.1