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DistilBERT-TC2000-10epochs

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distilbert
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+ ---
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+ license: apache-2.0
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+ base_model: distilbert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - recall
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+ - precision
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+ - f1
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+ model-index:
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+ - name: DistilBERT-TC2000-10epochs
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # DistilBERT-TC2000-10epochs
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+
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+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0752
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+ - Recall: {'recall': 0.98}
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+ - Precision: {'precision': 0.9803145941921073}
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+ - F1: {'f1': 0.9800242537313432}
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:---------------------------------:|:--------------------------:|
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+ | 1.0272 | 0.18 | 20 | 0.8815 | {'recall': 0.65} | {'precision': 0.7778791777580597} | {'f1': 0.6251215862860073} |
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+ | 0.8663 | 0.35 | 40 | 0.6770 | {'recall': 0.905} | {'precision': 0.9120308312976535} | {'f1': 0.9054010850819201} |
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+ | 0.6016 | 0.53 | 60 | 0.4088 | {'recall': 0.92} | {'precision': 0.9238949736347314} | {'f1': 0.9207242314918276} |
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+ | 0.3139 | 0.71 | 80 | 0.2508 | {'recall': 0.93} | {'precision': 0.9322386382325532} | {'f1': 0.929768888773222} |
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+ | 0.2645 | 0.88 | 100 | 0.2048 | {'recall': 0.955} | {'precision': 0.958280303030303} | {'f1': 0.954923196771023} |
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+ | 0.1811 | 1.06 | 120 | 0.1446 | {'recall': 0.965} | {'precision': 0.9675925925925927} | {'f1': 0.9648852158183796} |
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+ | 0.1429 | 1.24 | 140 | 0.1245 | {'recall': 0.975} | {'precision': 0.9762354497354496} | {'f1': 0.9749193929610656} |
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+ | 0.0941 | 1.42 | 160 | 0.1338 | {'recall': 0.965} | {'precision': 0.9683561643835616} | {'f1': 0.9652805623632961} |
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+ | 0.1242 | 1.59 | 180 | 0.0872 | {'recall': 0.975} | {'precision': 0.9759505494505496} | {'f1': 0.9750344590666455} |
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+ | 0.0893 | 1.77 | 200 | 0.0572 | {'recall': 0.985} | {'precision': 0.9853867102396515} | {'f1': 0.9849564819176908} |
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+ | 0.0477 | 1.95 | 220 | 0.0794 | {'recall': 0.975} | {'precision': 0.9762354497354496} | {'f1': 0.9749193929610656} |
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+ | 0.0128 | 2.12 | 240 | 0.0697 | {'recall': 0.98} | {'precision': 0.9807447665056361} | {'f1': 0.9799368665956859} |
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+ | 0.0449 | 2.3 | 260 | 0.0635 | {'recall': 0.97} | {'precision': 0.9725} | {'f1': 0.9702302752172594} |
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+ | 0.0996 | 2.48 | 280 | 0.0782 | {'recall': 0.97} | {'precision': 0.9725} | {'f1': 0.9700752508361203} |
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+ | 0.0328 | 2.65 | 300 | 0.0127 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
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+ | 0.0747 | 2.83 | 320 | 0.0380 | {'recall': 0.975} | {'precision': 0.9767605633802816} | {'f1': 0.9751792302987906} |
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+ | 0.0413 | 3.01 | 340 | 0.0127 | {'recall': 1.0} | {'precision': 1.0} | {'f1': 1.0} |
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+ | 0.0404 | 3.19 | 360 | 0.0120 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949915278995033} |
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+ | 0.0226 | 3.36 | 380 | 0.0085 | {'recall': 1.0} | {'precision': 1.0} | {'f1': 1.0} |
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+ | 0.0543 | 3.54 | 400 | 0.0139 | {'recall': 0.995} | {'precision': 0.9950925925925926} | {'f1': 0.9950042805165157} |
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+ | 0.0528 | 3.72 | 420 | 0.0408 | {'recall': 0.985} | {'precision': 0.9856521739130435} | {'f1': 0.9850251572327045} |
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+ | 0.0051 | 3.89 | 440 | 0.0808 | {'recall': 0.97} | {'precision': 0.9725} | {'f1': 0.9702302752172594} |
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+ | 0.014 | 4.07 | 460 | 0.0419 | {'recall': 0.985} | {'precision': 0.985241846323936} | {'f1': 0.985017255463425} |
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+ | 0.051 | 4.25 | 480 | 0.0127 | {'recall': 0.995} | {'precision': 0.9950925925925926} | {'f1': 0.9950042805165157} |
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+ | 0.0501 | 4.42 | 500 | 0.0200 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} |
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+ | 0.0062 | 4.6 | 520 | 0.0247 | {'recall': 0.985} | {'precision': 0.985241846323936} | {'f1': 0.985017255463425} |
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+ | 0.0118 | 4.78 | 540 | 0.0614 | {'recall': 0.975} | {'precision': 0.975962157809984} | {'f1': 0.975047977706797} |
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+ | 0.0348 | 4.96 | 560 | 0.0516 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0226 | 5.13 | 580 | 0.0144 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
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+ | 0.0159 | 5.31 | 600 | 0.0129 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
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+ | 0.0026 | 5.49 | 620 | 0.0176 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
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+ | 0.016 | 5.66 | 640 | 0.0404 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0433 | 5.84 | 660 | 0.0663 | {'recall': 0.975} | {'precision': 0.9756772575250836} | {'f1': 0.975041928721174} |
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+ | 0.0354 | 6.02 | 680 | 0.0253 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
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+ | 0.0041 | 6.19 | 700 | 0.0961 | {'recall': 0.97} | {'precision': 0.9711688311688311} | {'f1': 0.9700614296351452} |
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+ | 0.0579 | 6.37 | 720 | 0.1336 | {'recall': 0.965} | {'precision': 0.966783728687917} | {'f1': 0.9650813612906225} |
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+ | 0.0025 | 6.55 | 740 | 0.0424 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0328 | 6.73 | 760 | 0.0190 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} |
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+ | 0.0217 | 6.9 | 780 | 0.0488 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0096 | 7.08 | 800 | 0.1115 | {'recall': 0.97} | {'precision': 0.9711688311688311} | {'f1': 0.9700614296351452} |
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+ | 0.0106 | 7.26 | 820 | 0.0673 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0077 | 7.43 | 840 | 0.0354 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} |
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+ | 0.0222 | 7.61 | 860 | 0.0410 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0026 | 7.79 | 880 | 0.0590 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0576 | 7.96 | 900 | 0.0596 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.018 | 8.14 | 920 | 0.0428 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} |
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+ | 0.027 | 8.32 | 940 | 0.0425 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} |
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+ | 0.036 | 8.5 | 960 | 0.0341 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} |
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+ | 0.0094 | 8.67 | 980 | 0.0457 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0192 | 8.85 | 1000 | 0.0586 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.03 | 9.03 | 1020 | 0.0789 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0091 | 9.2 | 1040 | 0.0691 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0197 | 9.38 | 1060 | 0.0753 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0025 | 9.56 | 1080 | 0.0796 | {'recall': 0.975} | {'precision': 0.9756772575250836} | {'f1': 0.975041928721174} |
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+ | 0.0414 | 9.73 | 1100 | 0.0791 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+ | 0.0075 | 9.91 | 1120 | 0.0756 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.1
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+ - Tokenizers 0.13.3