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mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify

Text Generation
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mistral
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text-generation-inference
4-bit precision
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  1. README.md +32 -29
  2. eval_loss_plot.png +0 -0
  3. eval_precision_at_15_plot.png +0 -0
  4. model.safetensors +1 -1
  5. train_loss_plot.png +0 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -16,35 +16,35 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.0
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- - F1 Macro: 0.0
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- - Precision At 5: 0.2279
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- - Recall At 5: 0.0949
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- - Precision At 8: 0.1664
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- - Recall At 8: 0.1038
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- - Precision At 15: 0.1137
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- - Recall At 15: 0.1285
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- - Rare F1 Micro: 0.0
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- - Rare F1 Macro: 0.0
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- - Rare Precision: 0.0
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- - Rare Recall: 0.0
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- - Rare Precision At 5: 0.15
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- - Rare Recall At 5: 0.0645
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- - Rare Precision At 8: 0.1204
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- - Rare Recall At 8: 0.0788
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- - Rare Precision At 15: 0.0873
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- - Rare Recall At 15: 0.0997
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- - Not Rare F1 Micro: 0.5956
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- - Not Rare F1 Macro: 0.3733
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- - Not Rare Precision: 0.5956
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- - Not Rare Recall: 0.5956
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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: 0.1048
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  ## Model description
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@@ -71,18 +71,21 @@ The following hyperparameters were used during training:
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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: 500
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- - num_epochs: 4
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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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- | 0.6384 | 1.0 | 18 | 0.0 | 0.0 | 0.0368 | 0.0087 | 0.0377 | 0.0174 | 0.0377 | 0.0324 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0294 | 0.0081 | 0.0267 | 0.0107 | 0.0275 | 0.0211 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.2291 |
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- | 0.1265 | 2.0 | 36 | 0.0 | 0.0 | 0.0412 | 0.0096 | 0.0395 | 0.0166 | 0.0373 | 0.0303 | 0.0 | 0.0 | 0.0 | 0.0 | 0.025 | 0.0048 | 0.0276 | 0.0109 | 0.0284 | 0.0244 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1216 |
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- | 0.1092 | 3.0 | 54 | 0.0 | 0.0 | 0.1162 | 0.0391 | 0.1002 | 0.0595 | 0.0814 | 0.0890 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0603 | 0.0208 | 0.0579 | 0.0332 | 0.0564 | 0.0595 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1069 |
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- | 0.1033 | 3.7887 | 68 | 0.0 | 0.0 | 0.2279 | 0.0949 | 0.1664 | 0.1038 | 0.1137 | 0.1285 | 0.0 | 0.0 | 0.0 | 0.0 | 0.15 | 0.0645 | 0.1204 | 0.0788 | 0.0873 | 0.0997 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1048 |
 
 
 
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  ### Framework versions
 
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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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  - 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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