---
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: 0.0
- F1 Macro: 0.0
- Precision At 5: 0.2749
- Recall At 5: 0.0637
- Precision At 8: 0.2540
- Recall At 8: 0.0909
- Precision At 15: 0.1905
- Recall At 15: 0.1224
- Rare F1 Micro: 0.0
- Rare F1 Macro: 0.0
- Rare Precision: 0.0
- Rare Recall: 0.0
- Rare Precision At 5: 0.0037
- Rare Recall At 5: 0.0013
- Rare Precision At 8: 0.0043
- Rare Recall At 8: 0.0023
- Rare Precision At 15: 0.0049
- Rare Recall At 15: 0.0048
- Not Rare F1 Micro: 0.0
- Not Rare F1 Macro: 0.0
- Not Rare Precision: 0.0
- Not Rare Recall: 0.0
- Not Rare Precision At 5: 0.2742
- Not Rare Recall At 5: 0.1680
- Not Rare Precision At 8: 0.2540
- Not Rare Recall At 8: 0.2396
- Not Rare Precision At 15: 0.1906
- Not Rare Recall At 15: 0.3248
- Loss: 0.0209

## 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: 1
- 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 |
|:-------------:|:------:|:----:|:--------:|:--------:|:--------------:|:-----------:|:--------------:|:-----------:|:---------------:|:------------:|:-------------:|:-------------:|:--------------:|:-----------:|:-------------------:|:----------------:|:-------------------:|:----------------:|:--------------------:|:-----------------:|:-----------------:|:-----------------:|:------------------:|:---------------:|:-----------------------:|:--------------------:|:-----------------------:|:--------------------:|:------------------------:|:---------------------:|:---------------:|
| 0.023         | 0.9981 | 262  | 0.0      | 0.0      | 0.2749         | 0.0637      | 0.2540         | 0.0909      | 0.1905          | 0.1224       | 0.0           | 0.0           | 0.0            | 0.0         | 0.0037              | 0.0013           | 0.0043              | 0.0023           | 0.0049               | 0.0048            | 0.0               | 0.0               | 0.0                | 0.0             | 0.2742                  | 0.1680               | 0.2540                  | 0.2396               | 0.1906                   | 0.3248                | 0.0209          |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0
- Datasets 3.6.0
- Tokenizers 0.21.1