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bert-base-uncased_crows_pairs_classifieronly

Text Classification
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PyTorch
bert
Generated from Trainer
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - crows_pairs
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: bert-base-uncased_crows_pairs_classifieronly
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: crows_pairs
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+ type: crows_pairs
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+ config: crows_pairs
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+ split: test
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+ args: crows_pairs
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5364238410596026
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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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+ # bert-base-uncased_crows_pairs_classifieronly
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+
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+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the crows_pairs dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6924
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+ - Accuracy: 0.5364
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+ - Tp: 0.0066
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+ - Tn: 0.5298
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+ - Fp: 0.0033
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+ - Fn: 0.4603
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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: 0.0001
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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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: 50
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
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+ | 0.7219 | 1.05 | 20 | 0.7045 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.6962 | 2.11 | 40 | 0.6962 | 0.4669 | 0.4669 | 0.0 | 0.5331 | 0.0 |
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+ | 0.6983 | 3.16 | 60 | 0.6925 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.7018 | 4.21 | 80 | 0.6962 | 0.4669 | 0.4669 | 0.0 | 0.5331 | 0.0 |
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+ | 0.6972 | 5.26 | 100 | 0.6915 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.6964 | 6.32 | 120 | 0.6934 | 0.4801 | 0.1391 | 0.3411 | 0.1921 | 0.3278 |
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+ | 0.6983 | 7.37 | 140 | 0.6940 | 0.4636 | 0.3709 | 0.0927 | 0.4404 | 0.0960 |
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+ | 0.7025 | 8.42 | 160 | 0.6964 | 0.4669 | 0.4669 | 0.0 | 0.5331 | 0.0 |
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+ | 0.6958 | 9.47 | 180 | 0.6919 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.7079 | 10.53 | 200 | 0.7002 | 0.4669 | 0.4669 | 0.0 | 0.5331 | 0.0 |
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+ | 0.7033 | 11.58 | 220 | 0.6915 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.6932 | 12.63 | 240 | 0.6933 | 0.5 | 0.1060 | 0.3940 | 0.1391 | 0.3609 |
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+ | 0.7075 | 13.68 | 260 | 0.6919 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.695 | 14.74 | 280 | 0.6936 | 0.4371 | 0.1523 | 0.2848 | 0.2483 | 0.3146 |
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+ | 0.7068 | 15.79 | 300 | 0.6916 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.7007 | 16.84 | 320 | 0.6916 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.7035 | 17.89 | 340 | 0.6961 | 0.4669 | 0.4669 | 0.0 | 0.5331 | 0.0 |
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+ | 0.7002 | 18.95 | 360 | 0.6919 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.6992 | 20.0 | 380 | 0.6930 | 0.5166 | 0.0331 | 0.4834 | 0.0497 | 0.4338 |
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+ | 0.7024 | 21.05 | 400 | 0.6924 | 0.5364 | 0.0066 | 0.5298 | 0.0033 | 0.4603 |
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+ | 0.694 | 22.11 | 420 | 0.6949 | 0.4669 | 0.4603 | 0.0066 | 0.5265 | 0.0066 |
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+ | 0.7085 | 23.16 | 440 | 0.6928 | 0.5265 | 0.0199 | 0.5066 | 0.0265 | 0.4470 |
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+ | 0.6999 | 24.21 | 460 | 0.6936 | 0.4338 | 0.1457 | 0.2881 | 0.2450 | 0.3212 |
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+ | 0.6926 | 25.26 | 480 | 0.6921 | 0.5331 | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
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+ | 0.7088 | 26.32 | 500 | 0.6923 | 0.5331 | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
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+ | 0.6932 | 27.37 | 520 | 0.6922 | 0.5331 | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
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+ | 0.7011 | 28.42 | 540 | 0.6925 | 0.5364 | 0.0066 | 0.5298 | 0.0033 | 0.4603 |
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+ | 0.7016 | 29.47 | 560 | 0.6923 | 0.5331 | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
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+ | 0.7015 | 30.53 | 580 | 0.6925 | 0.5364 | 0.0099 | 0.5265 | 0.0066 | 0.4570 |
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+ | 0.7002 | 31.58 | 600 | 0.6929 | 0.5232 | 0.0331 | 0.4901 | 0.0430 | 0.4338 |
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+ | 0.701 | 32.63 | 620 | 0.6932 | 0.5099 | 0.0563 | 0.4536 | 0.0795 | 0.4106 |
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+ | 0.693 | 33.68 | 640 | 0.6921 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.711 | 34.74 | 660 | 0.6925 | 0.5364 | 0.0099 | 0.5265 | 0.0066 | 0.4570 |
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+ | 0.7013 | 35.79 | 680 | 0.6924 | 0.5331 | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
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+ | 0.6975 | 36.84 | 700 | 0.6916 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.7035 | 37.89 | 720 | 0.6918 | 0.5331 | 0.0 | 0.5331 | 0.0 | 0.4669 |
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+ | 0.6991 | 38.95 | 740 | 0.6929 | 0.5232 | 0.0298 | 0.4934 | 0.0397 | 0.4371 |
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+ | 0.7165 | 40.0 | 760 | 0.6923 | 0.5331 | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
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+ | 0.7029 | 41.05 | 780 | 0.6931 | 0.5066 | 0.0464 | 0.4603 | 0.0728 | 0.4205 |
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+ | 0.7021 | 42.11 | 800 | 0.6923 | 0.5331 | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
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+ | 0.6993 | 43.16 | 820 | 0.6935 | 0.4934 | 0.1291 | 0.3642 | 0.1689 | 0.3377 |
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+ | 0.7 | 44.21 | 840 | 0.6926 | 0.5331 | 0.0132 | 0.5199 | 0.0132 | 0.4536 |
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+ | 0.7023 | 45.26 | 860 | 0.6926 | 0.5331 | 0.0099 | 0.5232 | 0.0099 | 0.4570 |
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+ | 0.6961 | 46.32 | 880 | 0.6927 | 0.5232 | 0.0132 | 0.5099 | 0.0232 | 0.4536 |
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+ | 0.7014 | 47.37 | 900 | 0.6923 | 0.5331 | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
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+ | 0.7025 | 48.42 | 920 | 0.6924 | 0.5331 | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
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+ | 0.702 | 49.47 | 940 | 0.6924 | 0.5364 | 0.0066 | 0.5298 | 0.0033 | 0.4603 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1
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+ - Datasets 2.10.1
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+ - Tokenizers 0.13.2