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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-Instruct-v0.3 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify |
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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. |
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It achieves the following results on the evaluation set: |
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- F1 Micro: 0.0374 |
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- F1 Macro: 0.0013 |
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- Precision At 5: 0.2765 |
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- Recall At 5: 0.1166 |
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- Precision At 8: 0.2353 |
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- Recall At 8: 0.1441 |
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- Precision At 15: 0.1534 |
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- Recall At 15: 0.1748 |
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- Rare F1 Micro: 0.0115 |
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- Rare F1 Macro: 0.0007 |
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- Rare Precision: 0.3415 |
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- Rare Recall: 0.0059 |
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- Rare Precision At 5: 0.2324 |
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- Rare Recall At 5: 0.0985 |
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- Rare Precision At 8: 0.2004 |
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- Rare Recall At 8: 0.1223 |
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- Rare Precision At 15: 0.1265 |
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- Rare Recall At 15: 0.1500 |
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- Not Rare F1 Micro: 0.5 |
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- Not Rare F1 Macro: 0.5 |
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- Not Rare Precision: 0.5 |
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- Not Rare Recall: 0.5 |
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- Not Rare Precision At 5: 0.0809 |
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- Not Rare Recall At 5: 0.4044 |
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- Not Rare Precision At 8: 0.0506 |
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- Not Rare Recall At 8: 0.4044 |
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- Not Rare Precision At 15: 0.0270 |
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- Not Rare Recall At 15: 0.4044 |
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- Loss: 0.1015 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 7 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| 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 | |
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|:-------------:|:------:|:----:|:--------:|:--------:|:--------------:|:-----------:|:--------------:|:-----------:|:---------------:|:------------:|:-------------:|:-------------:|:--------------:|:-----------:|:-------------------:|:----------------:|:-------------------:|:----------------:|:--------------------:|:-----------------:|:-----------------:|:-----------------:|:------------------:|:---------------:|:-----------------------:|:--------------------:|:-----------------------:|:--------------------:|:------------------------:|:---------------------:|:---------------:| |
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| 0.0989 | 1.0 | 18 | 0.0 | 0.0 | 0.2721 | 0.1084 | 0.1958 | 0.1246 | 0.1270 | 0.1470 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2088 | 0.0885 | 0.1507 | 0.1015 | 0.0985 | 0.1201 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1039 | |
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| 0.1013 | 2.0 | 36 | 0.0 | 0.0 | 0.2706 | 0.1117 | 0.2307 | 0.1530 | 0.1485 | 0.1725 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2279 | 0.0919 | 0.1912 | 0.1342 | 0.1270 | 0.1506 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1054 | |
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| 0.1012 | 3.0 | 54 | 0.0 | 0.0 | 0.2765 | 0.1116 | 0.2371 | 0.1512 | 0.1480 | 0.1715 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2471 | 0.1051 | 0.1930 | 0.1354 | 0.125 | 0.1493 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1033 | |
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| 0.1003 | 4.0 | 72 | 0.0 | 0.0 | 0.2765 | 0.1166 | 0.2353 | 0.1441 | 0.1495 | 0.1722 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2456 | 0.1075 | 0.1939 | 0.1366 | 0.125 | 0.1491 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1017 | |
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| 0.0872 | 5.0 | 90 | 0.0238 | 0.0010 | 0.2765 | 0.1166 | 0.2353 | 0.1441 | 0.1534 | 0.1748 | 0.0033 | 0.0005 | 0.4 | 0.0017 | 0.2456 | 0.1075 | 0.2004 | 0.1223 | 0.1265 | 0.1500 | 0.5074 | 0.4995 | 0.5074 | 0.5074 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1042 | |
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| 0.0891 | 6.0 | 108 | 0.0417 | 0.0013 | 0.2765 | 0.1166 | 0.2353 | 0.1441 | 0.1534 | 0.1748 | 0.0131 | 0.0006 | 0.3077 | 0.0067 | 0.2368 | 0.1012 | 0.2004 | 0.1223 | 0.1265 | 0.1500 | 0.5074 | 0.5060 | 0.5074 | 0.5074 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1016 | |
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| 0.0844 | 6.6197 | 119 | 0.0374 | 0.0013 | 0.2765 | 0.1166 | 0.2353 | 0.1441 | 0.1534 | 0.1748 | 0.0115 | 0.0007 | 0.3415 | 0.0059 | 0.2324 | 0.0985 | 0.2004 | 0.1223 | 0.1265 | 0.1500 | 0.5 | 0.5 | 0.5 | 0.5 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1015 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.6.0 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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