--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-qnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue args: qnli metrics: - name: Accuracy type: accuracy value: 0.9086582463847702 --- # bert-base-cased-qnli This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.2569 - Accuracy: 0.9087 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6529 | 0.08 | 500 | 0.4945 | 0.7734 | | 0.4731 | 0.15 | 1000 | 0.3888 | 0.8406 | | 0.4113 | 0.23 | 1500 | 0.3605 | 0.8523 | | 0.4006 | 0.31 | 2000 | 0.3175 | 0.8649 | | 0.3615 | 0.38 | 2500 | 0.3220 | 0.8678 | | 0.3662 | 0.46 | 3000 | 0.3198 | 0.8667 | | 0.3578 | 0.53 | 3500 | 0.2970 | 0.8779 | | 0.3349 | 0.61 | 4000 | 0.2864 | 0.8834 | | 0.3548 | 0.69 | 4500 | 0.2664 | 0.8946 | | 0.3254 | 0.76 | 5000 | 0.2651 | 0.8929 | | 0.3212 | 0.84 | 5500 | 0.2943 | 0.8817 | | 0.3195 | 0.92 | 6000 | 0.2630 | 0.8960 | | 0.3044 | 0.99 | 6500 | 0.2766 | 0.8882 | | 0.245 | 1.07 | 7000 | 0.3268 | 0.9013 | | 0.2415 | 1.15 | 7500 | 0.2987 | 0.8991 | | 0.248 | 1.22 | 8000 | 0.3373 | 0.8812 | | 0.2328 | 1.3 | 8500 | 0.3474 | 0.8808 | | 0.2427 | 1.37 | 9000 | 0.2986 | 0.8913 | | 0.243 | 1.45 | 9500 | 0.2835 | 0.8982 | | 0.2375 | 1.53 | 10000 | 0.2893 | 0.8909 | | 0.2253 | 1.6 | 10500 | 0.2942 | 0.8960 | | 0.2429 | 1.68 | 11000 | 0.2852 | 0.9028 | | 0.238 | 1.76 | 11500 | 0.2672 | 0.9037 | | 0.2344 | 1.83 | 12000 | 0.2672 | 0.9021 | | 0.2368 | 1.91 | 12500 | 0.2754 | 0.9019 | | 0.2424 | 1.99 | 13000 | 0.2569 | 0.9087 | | 0.1542 | 2.06 | 13500 | 0.3807 | 0.9026 | | 0.1416 | 2.14 | 14000 | 0.3915 | 0.8980 | | 0.1445 | 2.21 | 14500 | 0.4292 | 0.8984 | | 0.1631 | 2.29 | 15000 | 0.4097 | 0.8971 | | 0.1512 | 2.37 | 15500 | 0.3880 | 0.9012 | | 0.1624 | 2.44 | 16000 | 0.4083 | 0.8955 | | 0.1616 | 2.52 | 16500 | 0.3950 | 0.9039 | | 0.1587 | 2.6 | 17000 | 0.3579 | 0.9103 | | 0.1615 | 2.67 | 17500 | 0.3931 | 0.9012 | | 0.1623 | 2.75 | 18000 | 0.3697 | 0.9059 | | 0.1687 | 2.83 | 18500 | 0.3473 | 0.9037 | | 0.1627 | 2.9 | 19000 | 0.3851 | 0.8982 | | 0.1593 | 2.98 | 19500 | 0.4039 | 0.9019 | | 0.1136 | 3.05 | 20000 | 0.4835 | 0.9024 | | 0.0965 | 3.13 | 20500 | 0.5061 | 0.9012 | | 0.0931 | 3.21 | 21000 | 0.5279 | 0.8991 | | 0.0993 | 3.28 | 21500 | 0.4856 | 0.9019 | | 0.1187 | 3.36 | 22000 | 0.4883 | 0.9032 | | 0.1008 | 3.44 | 22500 | 0.5054 | 0.9015 | | 0.1013 | 3.51 | 23000 | 0.5025 | 0.9023 | | 0.1092 | 3.59 | 23500 | 0.4485 | 0.8986 | | 0.1135 | 3.67 | 24000 | 0.5123 | 0.8977 | | 0.1042 | 3.74 | 24500 | 0.4884 | 0.9010 | | 0.1178 | 3.82 | 25000 | 0.4130 | 0.9006 | | 0.1041 | 3.89 | 25500 | 0.4847 | 0.8951 | | 0.1025 | 3.97 | 26000 | 0.4706 | 0.8986 | | 0.0833 | 4.05 | 26500 | 0.5018 | 0.9054 | | 0.0632 | 4.12 | 27000 | 0.5426 | 0.9055 | | 0.0613 | 4.2 | 27500 | 0.5629 | 0.9021 | | 0.0703 | 4.28 | 28000 | 0.5763 | 0.9033 | | 0.0687 | 4.35 | 28500 | 0.5274 | 0.9039 | | 0.0707 | 4.43 | 29000 | 0.5682 | 0.9019 | | 0.0717 | 4.51 | 29500 | 0.5382 | 0.9004 | | 0.0692 | 4.58 | 30000 | 0.5901 | 0.9010 | | 0.0701 | 4.66 | 30500 | 0.5817 | 0.8990 | | 0.0708 | 4.73 | 31000 | 0.5580 | 0.9019 | | 0.07 | 4.81 | 31500 | 0.5640 | 0.8990 | | 0.0725 | 4.89 | 32000 | 0.5768 | 0.8993 | | 0.0701 | 4.96 | 32500 | 0.5289 | 0.9035 | | 0.0441 | 5.04 | 33000 | 0.6401 | 0.9010 | | 0.0388 | 5.12 | 33500 | 0.6446 | 0.8991 | | 0.0417 | 5.19 | 34000 | 0.6327 | 0.9039 | | 0.039 | 5.27 | 34500 | 0.6385 | 0.9048 | | 0.0407 | 5.35 | 35000 | 0.6510 | 0.9030 | | 0.0446 | 5.42 | 35500 | 0.5788 | 0.9030 | | 0.0422 | 5.5 | 36000 | 0.6723 | 0.9024 | | 0.04 | 5.58 | 36500 | 0.6602 | 0.9037 | | 0.0514 | 5.65 | 37000 | 0.6407 | 0.9024 | | 0.0462 | 5.73 | 37500 | 0.6145 | 0.9048 | | 0.0479 | 5.8 | 38000 | 0.5881 | 0.9008 | | 0.0503 | 5.88 | 38500 | 0.6001 | 0.9008 | | 0.0385 | 5.96 | 39000 | 0.6464 | 0.9052 | | 0.0436 | 6.03 | 39500 | 0.6683 | 0.9039 | | 0.0296 | 6.11 | 40000 | 0.6965 | 0.9037 | | 0.0275 | 6.19 | 40500 | 0.7193 | 0.9039 | | 0.0258 | 6.26 | 41000 | 0.7229 | 0.9039 | | 0.0223 | 6.34 | 41500 | 0.6974 | 0.9074 | | 0.0268 | 6.42 | 42000 | 0.7045 | 0.9028 | | 0.0297 | 6.49 | 42500 | 0.7289 | 0.9055 | | 0.0295 | 6.57 | 43000 | 0.6810 | 0.9050 | | 0.0265 | 6.64 | 43500 | 0.6833 | 0.9065 | | 0.0268 | 6.72 | 44000 | 0.7155 | 0.9035 | | 0.0293 | 6.8 | 44500 | 0.7632 | 0.9015 | | 0.0289 | 6.87 | 45000 | 0.7229 | 0.9033 | | 0.0286 | 6.95 | 45500 | 0.6671 | 0.9054 | | 0.024 | 7.03 | 46000 | 0.7659 | 0.9033 | | 0.0141 | 7.1 | 46500 | 0.7981 | 0.9023 | | 0.0212 | 7.18 | 47000 | 0.7021 | 0.9088 | | 0.0183 | 7.26 | 47500 | 0.7122 | 0.9096 | | 0.0221 | 7.33 | 48000 | 0.7080 | 0.9065 | | 0.0146 | 7.41 | 48500 | 0.7344 | 0.9074 | | 0.0181 | 7.48 | 49000 | 0.7273 | 0.9105 | | 0.0161 | 7.56 | 49500 | 0.7332 | 0.9083 | | 0.0193 | 7.64 | 50000 | 0.7117 | 0.9094 | | 0.0165 | 7.71 | 50500 | 0.7797 | 0.9070 | | 0.0173 | 7.79 | 51000 | 0.7128 | 0.9107 | | 0.0158 | 7.87 | 51500 | 0.7420 | 0.9110 | | 0.0157 | 7.94 | 52000 | 0.7198 | 0.9129 | | 0.0142 | 8.02 | 52500 | 0.7033 | 0.9114 | | 0.0107 | 8.1 | 53000 | 0.7761 | 0.9109 | | 0.0122 | 8.17 | 53500 | 0.7777 | 0.9110 | | 0.013 | 8.25 | 54000 | 0.7880 | 0.9076 | | 0.0092 | 8.32 | 54500 | 0.8070 | 0.9074 | | 0.0102 | 8.4 | 55000 | 0.8113 | 0.9055 | | 0.0099 | 8.48 | 55500 | 0.8153 | 0.9079 | | 0.0087 | 8.55 | 56000 | 0.8045 | 0.9112 | | 0.0133 | 8.63 | 56500 | 0.8173 | 0.9081 | | 0.017 | 8.71 | 57000 | 0.7646 | 0.9101 | | 0.0093 | 8.78 | 57500 | 0.7681 | 0.9120 | | 0.0085 | 8.86 | 58000 | 0.8067 | 0.9083 | | 0.0137 | 8.94 | 58500 | 0.7645 | 0.9107 | | 0.0099 | 9.01 | 59000 | 0.7742 | 0.9123 | | 0.0074 | 9.09 | 59500 | 0.8052 | 0.9059 | | 0.0057 | 9.16 | 60000 | 0.7906 | 0.9132 | | 0.0046 | 9.24 | 60500 | 0.7915 | 0.9136 | | 0.0036 | 9.32 | 61000 | 0.8114 | 0.9101 | | 0.0036 | 9.39 | 61500 | 0.8382 | 0.9087 | | 0.0079 | 9.47 | 62000 | 0.8013 | 0.9110 | | 0.0079 | 9.55 | 62500 | 0.8116 | 0.9099 | | 0.0054 | 9.62 | 63000 | 0.8143 | 0.9110 | | 0.0036 | 9.7 | 63500 | 0.8138 | 0.9114 | | 0.0053 | 9.78 | 64000 | 0.8158 | 0.9107 | | 0.0035 | 9.85 | 64500 | 0.8146 | 0.9112 | | 0.0051 | 9.93 | 65000 | 0.8168 | 0.9107 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.7.1 - Datasets 1.18.3 - Tokenizers 0.11.6