Hugging Face's logo

deb101
/
mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify

Text Generation
Transformers
Safetensors
mistral
Generated from Trainer
conversational
text-generation-inference
4-bit precision
bitsandbytes
Model card Files
xet
Community
deb101 commited on
Commit
61a83a3
·
verified ·
1 Parent(s): 64f479e

Model save

Browse files
Files changed (6) hide show
  1. README.md +18 -24
  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
@@ -18,22 +18,22 @@ This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https
18
  It achieves the following results on the evaluation set:
19
  - F1 Micro: 0.0
20
  - F1 Macro: 0.0
21
- - Precision At 5: 0.2765
22
- - Recall At 5: 0.1167
23
- - Precision At 8: 0.2353
24
- - Recall At 8: 0.1441
25
- - Precision At 15: 0.1627
26
- - Recall At 15: 0.1927
27
  - Rare F1 Micro: 0.0
28
  - Rare F1 Macro: 0.0
29
  - Rare Precision: 0.0
30
  - Rare Recall: 0.0
31
- - Rare Precision At 5: 0.2397
32
- - Rare Recall At 5: 0.1023
33
- - Rare Precision At 8: 0.1967
34
- - Rare Recall At 8: 0.1289
35
- - Rare Precision At 15: 0.1397
36
- - Rare Recall At 15: 0.1722
37
  - Not Rare F1 Micro: 0.5956
38
  - Not Rare F1 Macro: 0.3733
39
  - Not Rare Precision: 0.5956
@@ -44,7 +44,7 @@ It achieves the following results on the evaluation set:
44
  - Not Rare Recall At 8: 0.4044
45
  - Not Rare Precision At 15: 0.0270
46
  - Not Rare Recall At 15: 0.4044
47
- - Loss: 0.1031
48
 
49
  ## Model description
50
 
@@ -72,23 +72,17 @@ The following hyperparameters were used during training:
72
  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
73
  - lr_scheduler_type: linear
74
  - lr_scheduler_warmup_steps: 500
75
- - num_epochs: 10
76
  - mixed_precision_training: Native AMP
77
 
78
  ### Training results
79
 
80
  | 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 |
81
  |:-------------:|:------:|:----:|:--------:|:--------:|:--------------:|:-----------:|:--------------:|:-----------:|:---------------:|:------------:|:-------------:|:-------------:|:--------------:|:-----------:|:-------------------:|:----------------:|:-------------------:|:----------------:|:--------------------:|:-----------------:|:-----------------:|:-----------------:|:------------------:|:---------------:|:-----------------------:|:--------------------:|:-----------------------:|:--------------------:|:------------------------:|:---------------------:|:---------------:|
82
- | 0.699 | 1.0 | 18 | 0.0 | 0.0 | 0.0588 | 0.0135 | 0.0506 | 0.0182 | 0.0441 | 0.0300 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0559 | 0.0128 | 0.0506 | 0.0183 | 0.0426 | 0.0294 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.2374 |
83
- | 0.1279 | 2.0 | 36 | 0.0 | 0.0 | 0.0529 | 0.0128 | 0.0432 | 0.0164 | 0.0436 | 0.0315 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0515 | 0.0120 | 0.0487 | 0.0188 | 0.0446 | 0.0320 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1224 |
84
- | 0.1087 | 3.0 | 54 | 0.0 | 0.0 | 0.0588 | 0.0136 | 0.0551 | 0.0206 | 0.0525 | 0.0404 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0632 | 0.0152 | 0.0551 | 0.0210 | 0.0466 | 0.0325 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1073 |
85
- | 0.1027 | 4.0 | 72 | 0.0 | 0.0 | 0.1485 | 0.0500 | 0.1278 | 0.0666 | 0.1025 | 0.0982 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1162 | 0.0336 | 0.0938 | 0.0423 | 0.0789 | 0.0657 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1043 |
86
- | 0.0973 | 5.0 | 90 | 0.0 | 0.0 | 0.25 | 0.0952 | 0.2105 | 0.1256 | 0.1505 | 0.1609 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2044 | 0.0795 | 0.1682 | 0.1019 | 0.1299 | 0.1405 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1041 |
87
- | 0.1023 | 6.0 | 108 | 0.0 | 0.0 | 0.2735 | 0.1098 | 0.2206 | 0.1379 | 0.1637 | 0.1803 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2279 | 0.0975 | 0.1811 | 0.1157 | 0.1417 | 0.1629 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1036 |
88
- | 0.1027 | 7.0 | 126 | 0.0 | 0.0 | 0.2838 | 0.1165 | 0.2325 | 0.1423 | 0.1588 | 0.1861 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2353 | 0.0997 | 0.1893 | 0.1234 | 0.1387 | 0.1698 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1038 |
89
- | 0.0994 | 8.0 | 144 | 0.0 | 0.0 | 0.2809 | 0.1176 | 0.2353 | 0.1441 | 0.1583 | 0.1850 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2426 | 0.1042 | 0.1930 | 0.1245 | 0.1382 | 0.1696 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1033 |
90
- | 0.1019 | 9.0 | 162 | 0.0 | 0.0 | 0.2809 | 0.1179 | 0.2353 | 0.1441 | 0.1618 | 0.1915 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2412 | 0.1026 | 0.1912 | 0.1240 | 0.1412 | 0.1725 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1035 |
91
- | 0.0961 | 9.4507 | 170 | 0.0 | 0.0 | 0.2765 | 0.1167 | 0.2353 | 0.1441 | 0.1627 | 0.1927 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2397 | 0.1023 | 0.1967 | 0.1289 | 0.1397 | 0.1722 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1031 |
92
 
93
 
94
  ### Framework versions
 
18
  It achieves the following results on the evaluation set:
19
  - F1 Micro: 0.0
20
  - F1 Macro: 0.0
21
+ - Precision At 5: 0.1676
22
+ - Recall At 5: 0.0597
23
+ - Precision At 8: 0.1379
24
+ - Recall At 8: 0.0770
25
+ - Precision At 15: 0.0926
26
+ - Recall At 15: 0.0992
27
  - Rare F1 Micro: 0.0
28
  - Rare F1 Macro: 0.0
29
  - Rare Precision: 0.0
30
  - Rare Recall: 0.0
31
+ - Rare Precision At 5: 0.1279
32
+ - Rare Recall At 5: 0.0462
33
+ - Rare Precision At 8: 0.0938
34
+ - Rare Recall At 8: 0.0522
35
+ - Rare Precision At 15: 0.0642
36
+ - Rare Recall At 15: 0.0700
37
  - Not Rare F1 Micro: 0.5956
38
  - Not Rare F1 Macro: 0.3733
39
  - Not Rare Precision: 0.5956
 
44
  - Not Rare Recall At 8: 0.4044
45
  - Not Rare Precision At 15: 0.0270
46
  - Not Rare Recall At 15: 0.4044
47
+ - Loss: 0.1050
48
 
49
  ## Model description
50
 
 
72
  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
73
  - lr_scheduler_type: linear
74
  - lr_scheduler_warmup_steps: 500
75
+ - num_epochs: 4
76
  - mixed_precision_training: Native AMP
77
 
78
  ### Training results
79
 
80
  | 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 |
81
  |:-------------:|:------:|:----:|:--------:|:--------:|:--------------:|:-----------:|:--------------:|:-----------:|:---------------:|:------------:|:-------------:|:-------------:|:--------------:|:-----------:|:-------------------:|:----------------:|:-------------------:|:----------------:|:--------------------:|:-----------------:|:-----------------:|:-----------------:|:------------------:|:---------------:|:-----------------------:|:--------------------:|:-----------------------:|:--------------------:|:------------------------:|:---------------------:|:---------------:|
82
+ | 0.624 | 1.0 | 18 | 0.0 | 0.0 | 0.0059 | 0.0013 | 0.0110 | 0.0037 | 0.0147 | 0.0111 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0103 | 0.0024 | 0.0129 | 0.0059 | 0.0142 | 0.0114 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.2217 |
83
+ | 0.1249 | 2.0 | 36 | 0.0 | 0.0 | 0.0088 | 0.0018 | 0.0110 | 0.0038 | 0.0152 | 0.0110 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0088 | 0.0018 | 0.0110 | 0.0047 | 0.0142 | 0.0124 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1219 |
84
+ | 0.1086 | 3.0 | 54 | 0.0 | 0.0 | 0.0471 | 0.0121 | 0.0506 | 0.0238 | 0.0456 | 0.0457 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0368 | 0.0106 | 0.0414 | 0.0198 | 0.0368 | 0.0370 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1074 |
85
+ | 0.1033 | 3.7887 | 68 | 0.0 | 0.0 | 0.1676 | 0.0597 | 0.1379 | 0.0770 | 0.0926 | 0.0992 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1279 | 0.0462 | 0.0938 | 0.0522 | 0.0642 | 0.0700 | 0.5956 | 0.3733 | 0.5956 | 0.5956 | 0.0809 | 0.4044 | 0.0506 | 0.4044 | 0.0270 | 0.4044 | 0.1050 |
 
 
 
 
 
 
86
 
87
 
88
  ### Framework versions
eval_loss_plot.png CHANGED
eval_precision_at_15_plot.png CHANGED
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:c174ed12a05a294f428c0a7ae5ce1c2f2871358269f5779e8ea7934d146f8f25
3
  size 4475046623
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:dc300542f1ef7575a3383fd670e2f235812f325d1998b4bc61f4352e4a569a40
3
  size 4475046623
train_loss_plot.png CHANGED
training_args.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:6efd72ce287b1ee5f5915a0296922089cd681a7c640f376dfd9732ee4ab00781
3
  size 5496
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:68f518bbf838e8860b2a510e1838c67e919184ab4e3e4294e2206c476f255956
3
  size 5496