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---
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: []
---
<!-- 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-instruct-v0.3-mimic4-adapt-multilabel-classify
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the None dataset.
It achieves the following results on the evaluation set:
- F1 Micro: 0.0005
- F1 Macro: 0.0000
- Precision At 5: 0.2847
- Recall At 5: 0.0664
- Precision At 8: 0.2542
- Recall At 8: 0.0910
- Precision At 15: 0.1926
- Recall At 15: 0.1252
- Rare F1 Micro: 0.0
- Rare F1 Macro: 0.0
- Rare Precision: 0.0
- Rare Recall: 0.0
- Rare Precision At 5: 0.0263
- Rare Recall At 5: 0.0069
- Rare Precision At 8: 0.0267
- Rare Recall At 8: 0.0118
- Rare Precision At 15: 0.0257
- Rare Recall At 15: 0.0219
- Not Rare F1 Micro: 0.0015
- Not Rare F1 Macro: 0.0003
- Not Rare Precision: 0.2576
- Not Rare Recall: 0.0007
- Not Rare Precision At 5: 0.2847
- Not Rare Recall At 5: 0.1756
- Not Rare Precision At 8: 0.2542
- Not Rare Recall At 8: 0.2401
- Not Rare Precision At 15: 0.1926
- Not Rare Recall At 15: 0.3324
- Loss: 0.0170
## 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 |
|:-------------:|:------:|:----:|:--------:|:--------:|:--------------:|:-----------:|:--------------:|:-----------:|:---------------:|:------------:|:-------------:|:-------------:|:--------------:|:-----------:|:-------------------:|:----------------:|:-------------------:|:----------------:|:--------------------:|:-----------------:|:-----------------:|:-----------------:|:------------------:|:---------------:|:-----------------------:|:--------------------:|:-----------------------:|:--------------------:|:------------------------:|:---------------------:|:---------------:|
| 0.0229 | 0.9981 | 262 | 0.0 | 0.0 | 0.2769 | 0.0644 | 0.2472 | 0.0880 | 0.1883 | 0.1226 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0078 | 0.0025 | 0.0075 | 0.0040 | 0.0070 | 0.0065 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2771 | 0.1707 | 0.2473 | 0.2341 | 0.1888 | 0.3243 | 0.0209 |
| 0.0181 | 1.9981 | 524 | 0.0 | 0.0 | 0.2775 | 0.0651 | 0.2542 | 0.0910 | 0.1934 | 0.1270 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0295 | 0.0091 | 0.0251 | 0.0128 | 0.0230 | 0.0214 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2774 | 0.1715 | 0.2542 | 0.2401 | 0.1933 | 0.3366 | 0.0170 |
| 0.0176 | 2.9981 | 786 | 0.0005 | 0.0000 | 0.2844 | 0.0665 | 0.2542 | 0.0910 | 0.1908 | 0.1227 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0282 | 0.0084 | 0.0246 | 0.0118 | 0.0273 | 0.0233 | 0.0013 | 0.0003 | 0.2830 | 0.0007 | 0.2844 | 0.1755 | 0.2542 | 0.2401 | 0.1908 | 0.3230 | 0.0170 |
| 0.0167 | 3.9981 | 1048 | 0.0005 | 0.0000 | 0.2847 | 0.0664 | 0.2542 | 0.0910 | 0.1926 | 0.1252 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0263 | 0.0069 | 0.0267 | 0.0118 | 0.0257 | 0.0219 | 0.0015 | 0.0003 | 0.2576 | 0.0007 | 0.2847 | 0.1756 | 0.2542 | 0.2401 | 0.1926 | 0.3324 | 0.0170 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0
- Datasets 3.6.0
- Tokenizers 0.21.1
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