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DistilBERT-TC2000-10epochs
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0752
- Recall: {'recall': 0.98}
- Precision: {'precision': 0.9803145941921073}
- F1: {'f1': 0.9800242537313432}
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: 16
- eval_batch_size: 16
- 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 | Recall | Precision | F1 |
|---|---|---|---|---|---|---|
| 1.0272 | 0.18 | 20 | 0.8815 | {'recall': 0.65} | {'precision': 0.7778791777580597} | {'f1': 0.6251215862860073} |
| 0.8663 | 0.35 | 40 | 0.6770 | {'recall': 0.905} | {'precision': 0.9120308312976535} | {'f1': 0.9054010850819201} |
| 0.6016 | 0.53 | 60 | 0.4088 | {'recall': 0.92} | {'precision': 0.9238949736347314} | {'f1': 0.9207242314918276} |
| 0.3139 | 0.71 | 80 | 0.2508 | {'recall': 0.93} | {'precision': 0.9322386382325532} | {'f1': 0.929768888773222} |
| 0.2645 | 0.88 | 100 | 0.2048 | {'recall': 0.955} | {'precision': 0.958280303030303} | {'f1': 0.954923196771023} |
| 0.1811 | 1.06 | 120 | 0.1446 | {'recall': 0.965} | {'precision': 0.9675925925925927} | {'f1': 0.9648852158183796} |
| 0.1429 | 1.24 | 140 | 0.1245 | {'recall': 0.975} | {'precision': 0.9762354497354496} | {'f1': 0.9749193929610656} |
| 0.0941 | 1.42 | 160 | 0.1338 | {'recall': 0.965} | {'precision': 0.9683561643835616} | {'f1': 0.9652805623632961} |
| 0.1242 | 1.59 | 180 | 0.0872 | {'recall': 0.975} | {'precision': 0.9759505494505496} | {'f1': 0.9750344590666455} |
| 0.0893 | 1.77 | 200 | 0.0572 | {'recall': 0.985} | {'precision': 0.9853867102396515} | {'f1': 0.9849564819176908} |
| 0.0477 | 1.95 | 220 | 0.0794 | {'recall': 0.975} | {'precision': 0.9762354497354496} | {'f1': 0.9749193929610656} |
| 0.0128 | 2.12 | 240 | 0.0697 | {'recall': 0.98} | {'precision': 0.9807447665056361} | {'f1': 0.9799368665956859} |
| 0.0449 | 2.3 | 260 | 0.0635 | {'recall': 0.97} | {'precision': 0.9725} | {'f1': 0.9702302752172594} |
| 0.0996 | 2.48 | 280 | 0.0782 | {'recall': 0.97} | {'precision': 0.9725} | {'f1': 0.9700752508361203} |
| 0.0328 | 2.65 | 300 | 0.0127 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
| 0.0747 | 2.83 | 320 | 0.0380 | {'recall': 0.975} | {'precision': 0.9767605633802816} | {'f1': 0.9751792302987906} |
| 0.0413 | 3.01 | 340 | 0.0127 | {'recall': 1.0} | {'precision': 1.0} | {'f1': 1.0} |
| 0.0404 | 3.19 | 360 | 0.0120 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949915278995033} |
| 0.0226 | 3.36 | 380 | 0.0085 | {'recall': 1.0} | {'precision': 1.0} | {'f1': 1.0} |
| 0.0543 | 3.54 | 400 | 0.0139 | {'recall': 0.995} | {'precision': 0.9950925925925926} | {'f1': 0.9950042805165157} |
| 0.0528 | 3.72 | 420 | 0.0408 | {'recall': 0.985} | {'precision': 0.9856521739130435} | {'f1': 0.9850251572327045} |
| 0.0051 | 3.89 | 440 | 0.0808 | {'recall': 0.97} | {'precision': 0.9725} | {'f1': 0.9702302752172594} |
| 0.014 | 4.07 | 460 | 0.0419 | {'recall': 0.985} | {'precision': 0.985241846323936} | {'f1': 0.985017255463425} |
| 0.051 | 4.25 | 480 | 0.0127 | {'recall': 0.995} | {'precision': 0.9950925925925926} | {'f1': 0.9950042805165157} |
| 0.0501 | 4.42 | 500 | 0.0200 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} |
| 0.0062 | 4.6 | 520 | 0.0247 | {'recall': 0.985} | {'precision': 0.985241846323936} | {'f1': 0.985017255463425} |
| 0.0118 | 4.78 | 540 | 0.0614 | {'recall': 0.975} | {'precision': 0.975962157809984} | {'f1': 0.975047977706797} |
| 0.0348 | 4.96 | 560 | 0.0516 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0226 | 5.13 | 580 | 0.0144 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
| 0.0159 | 5.31 | 600 | 0.0129 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
| 0.0026 | 5.49 | 620 | 0.0176 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
| 0.016 | 5.66 | 640 | 0.0404 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0433 | 5.84 | 660 | 0.0663 | {'recall': 0.975} | {'precision': 0.9756772575250836} | {'f1': 0.975041928721174} |
| 0.0354 | 6.02 | 680 | 0.0253 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
| 0.0041 | 6.19 | 700 | 0.0961 | {'recall': 0.97} | {'precision': 0.9711688311688311} | {'f1': 0.9700614296351452} |
| 0.0579 | 6.37 | 720 | 0.1336 | {'recall': 0.965} | {'precision': 0.966783728687917} | {'f1': 0.9650813612906225} |
| 0.0025 | 6.55 | 740 | 0.0424 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0328 | 6.73 | 760 | 0.0190 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
| 0.0217 | 6.9 | 780 | 0.0488 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0096 | 7.08 | 800 | 0.1115 | {'recall': 0.97} | {'precision': 0.9711688311688311} | {'f1': 0.9700614296351452} |
| 0.0106 | 7.26 | 820 | 0.0673 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0077 | 7.43 | 840 | 0.0354 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} |
| 0.0222 | 7.61 | 860 | 0.0410 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0026 | 7.79 | 880 | 0.0590 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0576 | 7.96 | 900 | 0.0596 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.018 | 8.14 | 920 | 0.0428 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} |
| 0.027 | 8.32 | 940 | 0.0425 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} |
| 0.036 | 8.5 | 960 | 0.0341 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} |
| 0.0094 | 8.67 | 980 | 0.0457 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0192 | 8.85 | 1000 | 0.0586 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.03 | 9.03 | 1020 | 0.0789 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0091 | 9.2 | 1040 | 0.0691 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0197 | 9.38 | 1060 | 0.0753 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0025 | 9.56 | 1080 | 0.0796 | {'recall': 0.975} | {'precision': 0.9756772575250836} | {'f1': 0.975041928721174} |
| 0.0414 | 9.73 | 1100 | 0.0791 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
| 0.0075 | 9.91 | 1120 | 0.0756 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
