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20230826123019

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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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+ - super_glue
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: '20230826123019'
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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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+ # 20230826123019
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+
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+ This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5900
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+ - Accuracy: 0.65
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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: 0.001
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 11
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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: 80.0
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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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 25 | 0.6011 | 0.66 |
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+ | No log | 2.0 | 50 | 0.5991 | 0.65 |
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+ | No log | 3.0 | 75 | 0.5983 | 0.65 |
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+ | No log | 4.0 | 100 | 0.6063 | 0.65 |
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+ | No log | 5.0 | 125 | 0.5973 | 0.65 |
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+ | No log | 6.0 | 150 | 0.6049 | 0.65 |
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+ | No log | 7.0 | 175 | 0.6031 | 0.65 |
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+ | No log | 8.0 | 200 | 0.6001 | 0.65 |
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+ | No log | 9.0 | 225 | 0.5969 | 0.64 |
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+ | No log | 10.0 | 250 | 0.6007 | 0.65 |
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+ | No log | 11.0 | 275 | 0.6016 | 0.65 |
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+ | No log | 12.0 | 300 | 0.5992 | 0.65 |
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+ | No log | 13.0 | 325 | 0.5968 | 0.65 |
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+ | No log | 14.0 | 350 | 0.5968 | 0.65 |
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+ | No log | 15.0 | 375 | 0.6000 | 0.65 |
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+ | No log | 16.0 | 400 | 0.6000 | 0.65 |
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+ | No log | 17.0 | 425 | 0.5883 | 0.66 |
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+ | No log | 18.0 | 450 | 0.5920 | 0.65 |
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+ | No log | 19.0 | 475 | 0.6035 | 0.62 |
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+ | 0.6519 | 20.0 | 500 | 0.6075 | 0.64 |
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+ | 0.6519 | 21.0 | 525 | 0.5919 | 0.65 |
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+ | 0.6519 | 22.0 | 550 | 0.5951 | 0.63 |
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+ | 0.6519 | 23.0 | 575 | 0.6037 | 0.61 |
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+ | 0.6519 | 24.0 | 600 | 0.6058 | 0.62 |
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+ | 0.6519 | 25.0 | 625 | 0.5944 | 0.65 |
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+ | 0.6519 | 26.0 | 650 | 0.5938 | 0.65 |
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+ | 0.6519 | 27.0 | 675 | 0.5909 | 0.66 |
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+ | 0.6519 | 28.0 | 700 | 0.5914 | 0.65 |
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+ | 0.6519 | 29.0 | 725 | 0.5902 | 0.66 |
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+ | 0.6519 | 30.0 | 750 | 0.5906 | 0.66 |
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+ | 0.6519 | 31.0 | 775 | 0.5936 | 0.65 |
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+ | 0.6519 | 32.0 | 800 | 0.5960 | 0.66 |
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+ | 0.6519 | 33.0 | 825 | 0.5953 | 0.65 |
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+ | 0.6519 | 34.0 | 850 | 0.5970 | 0.65 |
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+ | 0.6519 | 35.0 | 875 | 0.5937 | 0.65 |
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+ | 0.6519 | 36.0 | 900 | 0.5954 | 0.64 |
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+ | 0.6519 | 37.0 | 925 | 0.5993 | 0.63 |
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+ | 0.6519 | 38.0 | 950 | 0.5905 | 0.65 |
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+ | 0.6519 | 39.0 | 975 | 0.5898 | 0.65 |
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+ | 0.6395 | 40.0 | 1000 | 0.5947 | 0.65 |
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+ | 0.6395 | 41.0 | 1025 | 0.5966 | 0.64 |
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+ | 0.6395 | 42.0 | 1050 | 0.5953 | 0.65 |
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+ | 0.6395 | 43.0 | 1075 | 0.5968 | 0.64 |
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+ | 0.6395 | 44.0 | 1100 | 0.5934 | 0.65 |
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+ | 0.6395 | 45.0 | 1125 | 0.5948 | 0.66 |
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+ | 0.6395 | 46.0 | 1150 | 0.5958 | 0.65 |
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+ | 0.6395 | 47.0 | 1175 | 0.5928 | 0.65 |
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+ | 0.6395 | 48.0 | 1200 | 0.5922 | 0.65 |
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+ | 0.6395 | 49.0 | 1225 | 0.5929 | 0.65 |
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+ | 0.6395 | 50.0 | 1250 | 0.5967 | 0.64 |
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+ | 0.6395 | 51.0 | 1275 | 0.5908 | 0.65 |
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+ | 0.6395 | 52.0 | 1300 | 0.5930 | 0.66 |
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+ | 0.6395 | 53.0 | 1325 | 0.5910 | 0.65 |
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+ | 0.6395 | 54.0 | 1350 | 0.5931 | 0.65 |
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+ | 0.6395 | 55.0 | 1375 | 0.5900 | 0.66 |
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+ | 0.6395 | 56.0 | 1400 | 0.5925 | 0.65 |
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+ | 0.6395 | 57.0 | 1425 | 0.5938 | 0.66 |
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+ | 0.6395 | 58.0 | 1450 | 0.5963 | 0.65 |
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+ | 0.6395 | 59.0 | 1475 | 0.5955 | 0.64 |
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+ | 0.6331 | 60.0 | 1500 | 0.5935 | 0.65 |
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+ | 0.6331 | 61.0 | 1525 | 0.5937 | 0.66 |
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+ | 0.6331 | 62.0 | 1550 | 0.5924 | 0.65 |
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+ | 0.6331 | 63.0 | 1575 | 0.5909 | 0.65 |
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+ | 0.6331 | 64.0 | 1600 | 0.5891 | 0.65 |
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+ | 0.6331 | 65.0 | 1625 | 0.5881 | 0.65 |
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+ | 0.6331 | 66.0 | 1650 | 0.5884 | 0.65 |
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+ | 0.6331 | 67.0 | 1675 | 0.5893 | 0.65 |
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+ | 0.6331 | 68.0 | 1700 | 0.5900 | 0.65 |
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+ | 0.6331 | 69.0 | 1725 | 0.5908 | 0.65 |
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+ | 0.6331 | 70.0 | 1750 | 0.5912 | 0.65 |
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+ | 0.6331 | 71.0 | 1775 | 0.5914 | 0.65 |
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+ | 0.6331 | 72.0 | 1800 | 0.5901 | 0.65 |
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+ | 0.6331 | 73.0 | 1825 | 0.5898 | 0.65 |
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+ | 0.6331 | 74.0 | 1850 | 0.5896 | 0.65 |
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+ | 0.6331 | 75.0 | 1875 | 0.5905 | 0.65 |
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+ | 0.6331 | 76.0 | 1900 | 0.5901 | 0.65 |
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+ | 0.6331 | 77.0 | 1925 | 0.5901 | 0.65 |
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+ | 0.6331 | 78.0 | 1950 | 0.5900 | 0.65 |
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+ | 0.6331 | 79.0 | 1975 | 0.5900 | 0.65 |
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+ | 0.6276 | 80.0 | 2000 | 0.5900 | 0.65 |
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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 2.0.1+cu118
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.3