Hugging Face's logo

marieke93
/
MiniLM-evidence-types

Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
2
marieke93 commited on
Commit
7262c26
·
1 Parent(s): af4da85

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +26 -26
README.md CHANGED
@@ -16,11 +16,11 @@ should probably proofread and complete it, then remove this comment. -->
16
 
17
  This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset.
18
  It achieves the following results on the evaluation set:
19
- - Loss: 1.8672
20
- - Macro f1: 0.3726
21
- - Weighted f1: 0.7030
22
- - Accuracy: 0.7161
23
- - Balanced accuracy: 0.3616
24
 
25
  ## Model description
26
 
@@ -39,7 +39,7 @@ More information needed
39
  ### Training hyperparameters
40
 
41
  The following hyperparameters were used during training:
42
- - learning_rate: 2e-05
43
  - train_batch_size: 16
44
  - eval_batch_size: 16
45
  - seed: 42
@@ -52,26 +52,26 @@ The following hyperparameters were used during training:
52
 
53
  | Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
54
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
55
- | 1.4108 | 1.0 | 250 | 1.2698 | 0.1966 | 0.6084 | 0.6735 | 0.2195 |
56
- | 1.1452 | 2.0 | 500 | 1.0985 | 0.3484 | 0.6914 | 0.7116 | 0.3536 |
57
- | 0.9711 | 3.0 | 750 | 1.0901 | 0.2606 | 0.6413 | 0.6446 | 0.2932 |
58
- | 0.8437 | 4.0 | 1000 | 1.0197 | 0.2764 | 0.7024 | 0.7237 | 0.2783 |
59
- | 0.7186 | 5.0 | 1250 | 1.0895 | 0.2847 | 0.6824 | 0.6963 | 0.2915 |
60
- | 0.6312 | 6.0 | 1500 | 1.1296 | 0.3487 | 0.6888 | 0.6948 | 0.3377 |
61
- | 0.5311 | 7.0 | 1750 | 1.1515 | 0.3591 | 0.6982 | 0.7024 | 0.3496 |
62
- | 0.4737 | 8.0 | 2000 | 1.1962 | 0.3626 | 0.7185 | 0.7314 | 0.3415 |
63
- | 0.4047 | 9.0 | 2250 | 1.3313 | 0.3121 | 0.6920 | 0.7085 | 0.3033 |
64
- | 0.3753 | 10.0 | 2500 | 1.3993 | 0.3628 | 0.6976 | 0.7047 | 0.3495 |
65
- | 0.3217 | 11.0 | 2750 | 1.5078 | 0.3560 | 0.6958 | 0.7055 | 0.3464 |
66
- | 0.3079 | 12.0 | 3000 | 1.5875 | 0.3685 | 0.6968 | 0.7062 | 0.3514 |
67
- | 0.2623 | 13.0 | 3250 | 1.6470 | 0.3606 | 0.6976 | 0.7070 | 0.3490 |
68
- | 0.2393 | 14.0 | 3500 | 1.7164 | 0.3714 | 0.7069 | 0.7207 | 0.3551 |
69
- | 0.2335 | 15.0 | 3750 | 1.8151 | 0.3597 | 0.6975 | 0.7123 | 0.3466 |
70
- | 0.2255 | 16.0 | 4000 | 1.7838 | 0.3940 | 0.7034 | 0.7123 | 0.3869 |
71
- | 0.213 | 17.0 | 4250 | 1.8328 | 0.3725 | 0.6964 | 0.7062 | 0.3704 |
72
- | 0.1908 | 18.0 | 4500 | 1.8788 | 0.3708 | 0.7019 | 0.7154 | 0.3591 |
73
- | 0.1734 | 19.0 | 4750 | 1.8574 | 0.3752 | 0.7031 | 0.7161 | 0.3619 |
74
- | 0.1807 | 20.0 | 5000 | 1.8672 | 0.3726 | 0.7030 | 0.7161 | 0.3616 |
75
 
76
 
77
  ### Framework versions
 
16
 
17
  This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 2.4011
20
+ - Macro f1: 0.3527
21
+ - Weighted f1: 0.6956
22
+ - Accuracy: 0.7177
23
+ - Balanced accuracy: 0.3299
24
 
25
  ## Model description
26
 
 
39
  ### Training hyperparameters
40
 
41
  The following hyperparameters were used during training:
42
+ - learning_rate: 5e-05
43
  - train_batch_size: 16
44
  - eval_batch_size: 16
45
  - seed: 42
 
52
 
53
  | Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
54
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
55
+ | 1.2992 | 1.0 | 250 | 1.1977 | 0.1984 | 0.6212 | 0.6979 | 0.2104 |
56
+ | 1.1076 | 2.0 | 500 | 1.0809 | 0.2865 | 0.6479 | 0.6986 | 0.2924 |
57
+ | 0.912 | 3.0 | 750 | 1.1359 | 0.2677 | 0.6718 | 0.6804 | 0.2882 |
58
+ | 0.7969 | 4.0 | 1000 | 1.1522 | 0.2643 | 0.6840 | 0.7047 | 0.2692 |
59
+ | 0.6313 | 5.0 | 1250 | 1.2438 | 0.3176 | 0.6856 | 0.6986 | 0.3149 |
60
+ | 0.542 | 6.0 | 1500 | 1.3582 | 0.3212 | 0.6736 | 0.6872 | 0.3173 |
61
+ | 0.4401 | 7.0 | 1750 | 1.4300 | 0.3472 | 0.6921 | 0.7024 | 0.3305 |
62
+ | 0.382 | 8.0 | 2000 | 1.5530 | 0.3669 | 0.6965 | 0.7146 | 0.3480 |
63
+ | 0.309 | 9.0 | 2250 | 1.7972 | 0.3390 | 0.6777 | 0.6986 | 0.3174 |
64
+ | 0.2762 | 10.0 | 2500 | 1.7713 | 0.3745 | 0.6923 | 0.7161 | 0.3396 |
65
+ | 0.242 | 11.0 | 2750 | 1.9214 | 0.3672 | 0.6982 | 0.7215 | 0.3373 |
66
+ | 0.2112 | 12.0 | 3000 | 1.9624 | 0.3543 | 0.6917 | 0.7093 | 0.3310 |
67
+ | 0.179 | 13.0 | 3250 | 2.0087 | 0.3658 | 0.6922 | 0.7078 | 0.3431 |
68
+ | 0.1563 | 14.0 | 3500 | 2.1266 | 0.3554 | 0.7016 | 0.7237 | 0.3331 |
69
+ | 0.1531 | 15.0 | 3750 | 2.2341 | 0.3479 | 0.6951 | 0.7123 | 0.3284 |
70
+ | 0.115 | 16.0 | 4000 | 2.2671 | 0.3565 | 0.6970 | 0.7207 | 0.3308 |
71
+ | 0.115 | 17.0 | 4250 | 2.3446 | 0.3547 | 0.6988 | 0.7199 | 0.3342 |
72
+ | 0.0931 | 18.0 | 4500 | 2.3784 | 0.3570 | 0.6977 | 0.7169 | 0.3333 |
73
+ | 0.0886 | 19.0 | 4750 | 2.3871 | 0.3557 | 0.6970 | 0.7169 | 0.3325 |
74
+ | 0.0747 | 20.0 | 5000 | 2.4011 | 0.3527 | 0.6956 | 0.7177 | 0.3299 |
75
 
76
 
77
  ### Framework versions