File size: 10,082 Bytes
5d24ded 64bbbb3 5d24ded |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: google-bert/bert-base-uncased
metrics:
- accuracy
- precision
- recall
model-index:
- name: case-analysis-bert-base-uncased
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. -->
# case-analysis-bert-base-uncased
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8540
- Accuracy: 0.8129
- Precision: 0.7975
- Recall: 0.8129
- Precision Macro: 0.6427
- Recall Macro: 0.6184
- Macro Fpr: 0.0946
- Weighted Fpr: 0.0712
- Weighted Specificity: 0.8449
- Macro Specificity: 0.9145
- Weighted Sensitivity: 0.8129
- Macro Sensitivity: 0.6184
- F1 Micro: 0.8129
- F1 Macro: 0.6284
- F1 Weighted: 0.8035
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| No log | 1.0 | 224 | 0.7283 | 0.7862 | 0.7487 | 0.7862 | 0.5848 | 0.5572 | 0.1142 | 0.0831 | 0.8036 | 0.8974 | 0.7862 | 0.5572 | 0.7862 | 0.5606 | 0.7597 |
| No log | 2.0 | 448 | 0.8160 | 0.7996 | 0.7603 | 0.7996 | 0.5770 | 0.6065 | 0.0997 | 0.0771 | 0.8417 | 0.9103 | 0.7996 | 0.6065 | 0.7996 | 0.5914 | 0.7794 |
| 0.6512 | 3.0 | 672 | 0.8588 | 0.7906 | 0.7598 | 0.7906 | 0.5770 | 0.5989 | 0.1005 | 0.0811 | 0.8512 | 0.9105 | 0.7906 | 0.5989 | 0.7906 | 0.5840 | 0.7720 |
| 0.6512 | 4.0 | 896 | 1.0821 | 0.7817 | 0.7819 | 0.7817 | 0.6214 | 0.6429 | 0.0996 | 0.0851 | 0.8679 | 0.9124 | 0.7817 | 0.6429 | 0.7817 | 0.6299 | 0.7805 |
| 0.3466 | 5.0 | 1120 | 1.0612 | 0.8085 | 0.7999 | 0.8085 | 0.7129 | 0.6263 | 0.0948 | 0.0732 | 0.8470 | 0.9139 | 0.8085 | 0.6263 | 0.8085 | 0.6195 | 0.7928 |
| 0.3466 | 6.0 | 1344 | 1.2559 | 0.7929 | 0.7877 | 0.7929 | 0.6206 | 0.6362 | 0.0951 | 0.0801 | 0.8717 | 0.9161 | 0.7929 | 0.6362 | 0.7929 | 0.6273 | 0.7897 |
| 0.1715 | 7.0 | 1568 | 1.3701 | 0.7929 | 0.7889 | 0.7929 | 0.6345 | 0.6179 | 0.0991 | 0.0801 | 0.8558 | 0.9122 | 0.7929 | 0.6179 | 0.7929 | 0.6237 | 0.7893 |
| 0.1715 | 8.0 | 1792 | 1.4005 | 0.8107 | 0.8035 | 0.8107 | 0.6578 | 0.6370 | 0.0922 | 0.0722 | 0.8607 | 0.9179 | 0.8107 | 0.6370 | 0.8107 | 0.6464 | 0.8064 |
| 0.0636 | 9.0 | 2016 | 1.4737 | 0.8018 | 0.7881 | 0.8018 | 0.6583 | 0.6149 | 0.1026 | 0.0761 | 0.8271 | 0.9072 | 0.8018 | 0.6149 | 0.8018 | 0.6263 | 0.7896 |
| 0.0636 | 10.0 | 2240 | 1.7569 | 0.7884 | 0.7962 | 0.7884 | 0.6275 | 0.6428 | 0.0960 | 0.0821 | 0.8750 | 0.9158 | 0.7884 | 0.6428 | 0.7884 | 0.6332 | 0.7909 |
| 0.0636 | 11.0 | 2464 | 1.7141 | 0.7906 | 0.7824 | 0.7906 | 0.6166 | 0.6083 | 0.1035 | 0.0811 | 0.8424 | 0.9083 | 0.7906 | 0.6083 | 0.7906 | 0.6101 | 0.7845 |
| 0.0159 | 12.0 | 2688 | 1.7144 | 0.7951 | 0.7914 | 0.7951 | 0.6393 | 0.6413 | 0.0969 | 0.0791 | 0.8610 | 0.9140 | 0.7951 | 0.6413 | 0.7951 | 0.6373 | 0.7917 |
| 0.0159 | 13.0 | 2912 | 1.7243 | 0.7996 | 0.7969 | 0.7996 | 0.6535 | 0.6526 | 0.0942 | 0.0771 | 0.8638 | 0.9158 | 0.7996 | 0.6526 | 0.7996 | 0.6529 | 0.7982 |
| 0.0043 | 14.0 | 3136 | 1.8551 | 0.7973 | 0.7948 | 0.7973 | 0.6576 | 0.6189 | 0.1041 | 0.0781 | 0.8314 | 0.9072 | 0.7973 | 0.6189 | 0.7973 | 0.6335 | 0.7912 |
| 0.0043 | 15.0 | 3360 | 1.8841 | 0.7929 | 0.7869 | 0.7929 | 0.6154 | 0.6162 | 0.1008 | 0.0801 | 0.8511 | 0.9110 | 0.7929 | 0.6162 | 0.7929 | 0.6104 | 0.7861 |
| 0.0029 | 16.0 | 3584 | 2.0853 | 0.7550 | 0.7837 | 0.7550 | 0.6010 | 0.6119 | 0.1100 | 0.0976 | 0.8698 | 0.9062 | 0.7550 | 0.6119 | 0.7550 | 0.6015 | 0.7661 |
| 0.0029 | 17.0 | 3808 | 1.9722 | 0.7840 | 0.7783 | 0.7840 | 0.6018 | 0.5839 | 0.1076 | 0.0841 | 0.8394 | 0.9059 | 0.7840 | 0.5839 | 0.7840 | 0.5917 | 0.7797 |
| 0.0071 | 18.0 | 4032 | 1.8735 | 0.7996 | 0.7783 | 0.7996 | 0.6086 | 0.5917 | 0.1053 | 0.0771 | 0.8193 | 0.9047 | 0.7996 | 0.5917 | 0.7996 | 0.5960 | 0.7840 |
| 0.0071 | 19.0 | 4256 | 1.8294 | 0.8018 | 0.7840 | 0.8018 | 0.6114 | 0.5943 | 0.1025 | 0.0761 | 0.8308 | 0.9082 | 0.8018 | 0.5943 | 0.8018 | 0.6001 | 0.7895 |
| 0.0071 | 20.0 | 4480 | 1.8578 | 0.7973 | 0.7939 | 0.7973 | 0.6367 | 0.6232 | 0.0990 | 0.0781 | 0.8497 | 0.9118 | 0.7973 | 0.6232 | 0.7973 | 0.6285 | 0.7942 |
| 0.0049 | 21.0 | 4704 | 1.8770 | 0.7973 | 0.7939 | 0.7973 | 0.6367 | 0.6232 | 0.0990 | 0.0781 | 0.8497 | 0.9118 | 0.7973 | 0.6232 | 0.7973 | 0.6285 | 0.7942 |
| 0.0049 | 22.0 | 4928 | 1.8932 | 0.7951 | 0.7876 | 0.7951 | 0.6219 | 0.6119 | 0.1007 | 0.0791 | 0.8461 | 0.9103 | 0.7951 | 0.6119 | 0.7951 | 0.6155 | 0.7900 |
| 0.0015 | 23.0 | 5152 | 1.9834 | 0.7996 | 0.7965 | 0.7996 | 0.6441 | 0.6389 | 0.0960 | 0.0771 | 0.8599 | 0.9149 | 0.7996 | 0.6389 | 0.7996 | 0.6403 | 0.7971 |
| 0.0015 | 24.0 | 5376 | 1.9926 | 0.8018 | 0.7984 | 0.8018 | 0.6468 | 0.6399 | 0.0952 | 0.0761 | 0.8603 | 0.9155 | 0.8018 | 0.6399 | 0.8018 | 0.6422 | 0.7991 |
| 0.0001 | 25.0 | 5600 | 1.9771 | 0.7973 | 0.7790 | 0.7973 | 0.6025 | 0.6024 | 0.1011 | 0.0781 | 0.8420 | 0.9098 | 0.7973 | 0.6024 | 0.7973 | 0.6017 | 0.7871 |
| 0.0001 | 26.0 | 5824 | 1.9871 | 0.7951 | 0.7770 | 0.7951 | 0.5996 | 0.6015 | 0.1020 | 0.0791 | 0.8416 | 0.9092 | 0.7951 | 0.6015 | 0.7951 | 0.5997 | 0.7850 |
| 0.0 | 27.0 | 6048 | 1.8756 | 0.8129 | 0.7961 | 0.8129 | 0.6440 | 0.6200 | 0.0939 | 0.0712 | 0.8462 | 0.9148 | 0.8129 | 0.6200 | 0.8129 | 0.6293 | 0.8029 |
| 0.0 | 28.0 | 6272 | 1.8473 | 0.8151 | 0.7998 | 0.8151 | 0.6463 | 0.6194 | 0.0937 | 0.0703 | 0.8453 | 0.9151 | 0.8151 | 0.6194 | 0.8151 | 0.6305 | 0.8056 |
| 0.0 | 29.0 | 6496 | 1.8525 | 0.8129 | 0.7975 | 0.8129 | 0.6427 | 0.6184 | 0.0946 | 0.0712 | 0.8449 | 0.9145 | 0.8129 | 0.6184 | 0.8129 | 0.6284 | 0.8035 |
| 0.0001 | 30.0 | 6720 | 1.8540 | 0.8129 | 0.7975 | 0.8129 | 0.6427 | 0.6184 | 0.0946 | 0.0712 | 0.8449 | 0.9145 | 0.8129 | 0.6184 | 0.8129 | 0.6284 | 0.8035 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|