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bert-finetuned-MedicalChunkSecond
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1804
- Precision: 0.2396
- Recall: 0.2613
- F1: 0.25
- Accuracy: 0.9541
- Pop Precision: 0.3889
- Pop Recall: 0.3818
- Pop F1: 0.3853
- Pop Number: 55
- Int Precision: 0.2065
- Int Recall: 0.2468
- Int F1: 0.2249
- Int Number: 77
- Out Precision: 0.1690
- Out Recall: 0.1791
- Out F1: 0.1739
- Out Number: 67
- Pop Count: 121
- Int Count: 254
- Out Count: 158
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: 2e-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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Pop Precision | Pop Recall | Pop F1 | Pop Number | Int Precision | Int Recall | Int F1 | Int Number | Out Precision | Out Recall | Out F1 | Out Number | Pop Count | Int Count | Out Count |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 56 | 0.1626 | 0.0 | 0.0 | 0.0 | 0.9599 | 0.0 | 0.0 | 0.0 | 55 | 0.0 | 0.0 | 0.0 | 77 | 0.0 | 0.0 | 0.0 | 67 | 1 | 57 | 0 |
| No log | 2.0 | 112 | 0.1420 | 0.0962 | 0.0503 | 0.0660 | 0.9575 | 0.0588 | 0.0182 | 0.0278 | 55 | 0.1084 | 0.1169 | 0.1125 | 77 | 0.0 | 0.0 | 0.0 | 67 | 24 | 193 | 4 |
| No log | 3.0 | 168 | 0.1354 | 0.1604 | 0.1508 | 0.1554 | 0.9568 | 0.2449 | 0.2182 | 0.2308 | 55 | 0.1204 | 0.1688 | 0.1405 | 77 | 0.1667 | 0.0746 | 0.1031 | 67 | 107 | 261 | 49 |
| No log | 4.0 | 224 | 0.1360 | 0.2701 | 0.1859 | 0.2202 | 0.9620 | 0.3478 | 0.2909 | 0.3168 | 55 | 0.1961 | 0.1299 | 0.1562 | 77 | 0.275 | 0.1642 | 0.2056 | 67 | 98 | 137 | 77 |
| No log | 5.0 | 280 | 0.1443 | 0.2914 | 0.2563 | 0.2727 | 0.9603 | 0.4038 | 0.3818 | 0.3925 | 55 | 0.2289 | 0.2468 | 0.2375 | 77 | 0.275 | 0.1642 | 0.2056 | 67 | 121 | 199 | 85 |
| No log | 6.0 | 336 | 0.1618 | 0.2988 | 0.2462 | 0.2700 | 0.9601 | 0.4865 | 0.3273 | 0.3913 | 55 | 0.2571 | 0.2338 | 0.2449 | 77 | 0.2281 | 0.1940 | 0.2097 | 67 | 85 | 187 | 121 |
| No log | 7.0 | 392 | 0.1622 | 0.2417 | 0.2563 | 0.2488 | 0.9571 | 0.3333 | 0.3636 | 0.3478 | 55 | 0.2125 | 0.2208 | 0.2166 | 77 | 0.1972 | 0.2090 | 0.2029 | 67 | 126 | 213 | 142 |
| No log | 8.0 | 448 | 0.1741 | 0.2356 | 0.2663 | 0.25 | 0.9544 | 0.3667 | 0.4 | 0.3826 | 55 | 0.1919 | 0.2468 | 0.2159 | 77 | 0.1818 | 0.1791 | 0.1805 | 67 | 132 | 258 | 147 |
| 0.1112 | 9.0 | 504 | 0.1796 | 0.2275 | 0.2663 | 0.2454 | 0.9527 | 0.3929 | 0.4 | 0.3964 | 55 | 0.1845 | 0.2468 | 0.2111 | 77 | 0.1622 | 0.1791 | 0.1702 | 67 | 129 | 278 | 158 |
| 0.1112 | 10.0 | 560 | 0.1804 | 0.2396 | 0.2613 | 0.25 | 0.9541 | 0.3889 | 0.3818 | 0.3853 | 55 | 0.2065 | 0.2468 | 0.2249 | 77 | 0.1690 | 0.1791 | 0.1739 | 67 | 121 | 254 | 158 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
