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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: 0.0062
- F1 Macro: 0.0059
- Precision At 5: 0.0131
- Recall At 5: 0.0040
- Precision At 8: 0.0108
- Recall At 8: 0.0056
- Precision At 15: 0.0124
- Recall At 15: 0.0101
- Rare F1 Micro: 0.0040
- Rare F1 Macro: 0.0040
- Rare Precision: 0.0020
- Rare Recall: 0.9992
- Rare Precision At 5: 0.0055
- Rare Recall At 5: 0.0025
- Rare Precision At 8: 0.0041
- Rare Recall At 8: 0.0029
- Rare Precision At 15: 0.0032
- Rare Recall At 15: 0.0044
- Not Rare F1 Micro: 0.1354
- Not Rare F1 Macro: 0.1308
- Not Rare Precision: 0.0726
- Not Rare Recall: 0.9998
- Not Rare Precision At 5: 0.1391
- Not Rare Recall At 5: 0.0842
- Not Rare Precision At 8: 0.1066
- Not Rare Recall At 8: 0.1005
- Not Rare Precision At 15: 0.0989
- Not Rare Recall At 15: 0.1650
- Loss: -2.3104
## 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: 5
- 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 |
|:-------------:|:------:|:----:|:--------:|:--------:|:--------------:|:-----------:|:--------------:|:-----------:|:---------------:|:------------:|:-------------:|:-------------:|:--------------:|:-----------:|:-------------------:|:----------------:|:-------------------:|:----------------:|:--------------------:|:-----------------:|:-----------------:|:-----------------:|:------------------:|:---------------:|:-----------------------:|:--------------------:|:-----------------------:|:--------------------:|:------------------------:|:---------------------:|:---------------:|
| -2.5733 | 0.9981 | 262 | 0.0086 | 0.0060 | 0.2032 | 0.0452 | 0.1975 | 0.0694 | 0.1826 | 0.1185 | 0.0051 | 0.0040 | 0.0026 | 0.7894 | 0.0369 | 0.0112 | 0.0329 | 0.0162 | 0.0290 | 0.0270 | 0.1354 | 0.1308 | 0.0726 | 1.0 | 0.2012 | 0.1187 | 0.1963 | 0.1842 | 0.1802 | 0.3115 | -2.1808 |
| -2.8745 | 1.9981 | 524 | 0.0070 | 0.0062 | 0.1153 | 0.0311 | 0.1079 | 0.0456 | 0.0933 | 0.0723 | 0.0044 | 0.0041 | 0.0022 | 0.8685 | 0.0391 | 0.0155 | 0.0333 | 0.0210 | 0.0281 | 0.0323 | 0.1399 | 0.1337 | 0.0754 | 0.9720 | 0.1735 | 0.1110 | 0.1544 | 0.1553 | 0.1400 | 0.2550 | -2.2971 |
| -3.0665 | 2.9981 | 786 | 0.0064 | 0.0060 | 0.0525 | 0.0148 | 0.0450 | 0.0203 | 0.0392 | 0.0309 | 0.0041 | 0.0040 | 0.0020 | 0.9688 | 0.0150 | 0.0061 | 0.0134 | 0.0086 | 0.0107 | 0.0129 | 0.1376 | 0.1323 | 0.0739 | 0.9840 | 0.1498 | 0.0950 | 0.1236 | 0.1245 | 0.1147 | 0.2041 | -2.3224 |
| -3.5627 | 3.9981 | 1048 | 0.0062 | 0.0060 | 0.0182 | 0.0059 | 0.0152 | 0.0075 | 0.0163 | 0.0135 | 0.0040 | 0.0040 | 0.0020 | 0.9920 | 0.0069 | 0.0031 | 0.0052 | 0.0039 | 0.0044 | 0.0062 | 0.1361 | 0.1313 | 0.0730 | 0.9973 | 0.1394 | 0.0855 | 0.1093 | 0.1055 | 0.1022 | 0.1756 | -2.3239 |
| -4.0526 | 4.9981 | 1310 | 0.0062 | 0.0059 | 0.0131 | 0.0040 | 0.0108 | 0.0056 | 0.0124 | 0.0101 | 0.0040 | 0.0040 | 0.0020 | 0.9992 | 0.0055 | 0.0025 | 0.0041 | 0.0029 | 0.0032 | 0.0044 | 0.1354 | 0.1308 | 0.0726 | 0.9998 | 0.1391 | 0.0842 | 0.1066 | 0.1005 | 0.0989 | 0.1650 | -2.3104 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0
- Datasets 3.6.0
- Tokenizers 0.21.1
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