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simonycl
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roberta-large-sst-2-32-13

Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Inference Endpoints
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+ ---
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+ license: mit
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+ base_model: roberta-large
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: roberta-large-sst-2-32-13
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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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+ # roberta-large-sst-2-32-13
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+
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+ This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4497
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+ - Accuracy: 0.9375
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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: 1e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 150
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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 | 2 | 0.6944 | 0.5 |
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+ | No log | 2.0 | 4 | 0.6944 | 0.5 |
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+ | No log | 3.0 | 6 | 0.6944 | 0.5 |
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+ | No log | 4.0 | 8 | 0.6944 | 0.5 |
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+ | 0.7018 | 5.0 | 10 | 0.6944 | 0.5 |
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+ | 0.7018 | 6.0 | 12 | 0.6943 | 0.5 |
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+ | 0.7018 | 7.0 | 14 | 0.6943 | 0.5 |
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+ | 0.7018 | 8.0 | 16 | 0.6942 | 0.5 |
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+ | 0.7018 | 9.0 | 18 | 0.6941 | 0.5 |
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+ | 0.7003 | 10.0 | 20 | 0.6940 | 0.5 |
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+ | 0.7003 | 11.0 | 22 | 0.6939 | 0.5 |
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+ | 0.7003 | 12.0 | 24 | 0.6938 | 0.5 |
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+ | 0.7003 | 13.0 | 26 | 0.6937 | 0.5 |
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+ | 0.7003 | 14.0 | 28 | 0.6936 | 0.5 |
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+ | 0.6964 | 15.0 | 30 | 0.6934 | 0.5 |
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+ | 0.6964 | 16.0 | 32 | 0.6934 | 0.5 |
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+ | 0.6964 | 17.0 | 34 | 0.6933 | 0.5 |
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+ | 0.6964 | 18.0 | 36 | 0.6932 | 0.5 |
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+ | 0.6964 | 19.0 | 38 | 0.6931 | 0.5 |
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+ | 0.7001 | 20.0 | 40 | 0.6931 | 0.5 |
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+ | 0.7001 | 21.0 | 42 | 0.6931 | 0.5 |
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+ | 0.7001 | 22.0 | 44 | 0.6931 | 0.5 |
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+ | 0.7001 | 23.0 | 46 | 0.6931 | 0.5 |
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+ | 0.7001 | 24.0 | 48 | 0.6931 | 0.5 |
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+ | 0.6924 | 25.0 | 50 | 0.6931 | 0.5 |
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+ | 0.6924 | 26.0 | 52 | 0.6931 | 0.5 |
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+ | 0.6924 | 27.0 | 54 | 0.6931 | 0.5 |
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+ | 0.6924 | 28.0 | 56 | 0.6930 | 0.5 |
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+ | 0.6924 | 29.0 | 58 | 0.6930 | 0.5 |
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+ | 0.6985 | 30.0 | 60 | 0.6930 | 0.5 |
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+ | 0.6985 | 31.0 | 62 | 0.6930 | 0.5 |
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+ | 0.6985 | 32.0 | 64 | 0.6929 | 0.5 |
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+ | 0.6985 | 33.0 | 66 | 0.6927 | 0.5 |
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+ | 0.6985 | 34.0 | 68 | 0.6925 | 0.5 |
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+ | 0.6968 | 35.0 | 70 | 0.6924 | 0.5 |
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+ | 0.6968 | 36.0 | 72 | 0.6923 | 0.5 |
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+ | 0.6968 | 37.0 | 74 | 0.6922 | 0.5 |
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+ | 0.6968 | 38.0 | 76 | 0.6922 | 0.5 |
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+ | 0.6968 | 39.0 | 78 | 0.6920 | 0.5 |
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+ | 0.6822 | 40.0 | 80 | 0.6917 | 0.5 |
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+ | 0.6822 | 41.0 | 82 | 0.6916 | 0.5 |
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+ | 0.6822 | 42.0 | 84 | 0.6913 | 0.5 |
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+ | 0.6822 | 43.0 | 86 | 0.6911 | 0.5 |
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+ | 0.6822 | 44.0 | 88 | 0.6910 | 0.5 |
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+ | 0.6907 | 45.0 | 90 | 0.6908 | 0.5 |
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+ | 0.6907 | 46.0 | 92 | 0.6906 | 0.5 |
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+ | 0.6907 | 47.0 | 94 | 0.6905 | 0.5 |
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+ | 0.6907 | 48.0 | 96 | 0.6902 | 0.5156 |
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+ | 0.6907 | 49.0 | 98 | 0.6898 | 0.5625 |
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+ | 0.6822 | 50.0 | 100 | 0.6892 | 0.5469 |
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+ | 0.6822 | 51.0 | 102 | 0.6887 | 0.5938 |
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+ | 0.6822 | 52.0 | 104 | 0.6881 | 0.5938 |
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+ | 0.6822 | 53.0 | 106 | 0.6874 | 0.6094 |
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+ | 0.6822 | 54.0 | 108 | 0.6868 | 0.6094 |
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+ | 0.6744 | 55.0 | 110 | 0.6862 | 0.5938 |
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+ | 0.6744 | 56.0 | 112 | 0.6859 | 0.5312 |
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+ | 0.6744 | 57.0 | 114 | 0.6856 | 0.5469 |
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+ | 0.6744 | 58.0 | 116 | 0.6873 | 0.5469 |
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+ | 0.6744 | 59.0 | 118 | 0.6910 | 0.5469 |
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+ | 0.6401 | 60.0 | 120 | 0.6938 | 0.5469 |
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+ | 0.6401 | 61.0 | 122 | 0.6911 | 0.5625 |
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+ | 0.6401 | 62.0 | 124 | 0.6835 | 0.5625 |
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+ | 0.6401 | 63.0 | 126 | 0.6765 | 0.5781 |
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+ | 0.6401 | 64.0 | 128 | 0.6689 | 0.5781 |
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+ | 0.5823 | 65.0 | 130 | 0.6597 | 0.6094 |
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+ | 0.5823 | 66.0 | 132 | 0.6514 | 0.625 |
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+ | 0.5823 | 67.0 | 134 | 0.6459 | 0.6406 |
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+ | 0.5823 | 68.0 | 136 | 0.6372 | 0.6562 |
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+ | 0.5823 | 69.0 | 138 | 0.6274 | 0.6562 |
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+ | 0.5265 | 70.0 | 140 | 0.6163 | 0.6875 |
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+ | 0.5265 | 71.0 | 142 | 0.6018 | 0.7188 |
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+ | 0.5265 | 72.0 | 144 | 0.5853 | 0.7812 |
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+ | 0.5265 | 73.0 | 146 | 0.5600 | 0.7812 |
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+ | 0.5265 | 74.0 | 148 | 0.5138 | 0.8125 |
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+ | 0.4305 | 75.0 | 150 | 0.4514 | 0.8594 |
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+ | 0.4305 | 76.0 | 152 | 0.3753 | 0.9219 |
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+ | 0.4305 | 77.0 | 154 | 0.3197 | 0.9375 |
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+ | 0.4305 | 78.0 | 156 | 0.2687 | 0.9375 |
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+ | 0.4305 | 79.0 | 158 | 0.2246 | 0.9531 |
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+ | 0.2335 | 80.0 | 160 | 0.2019 | 0.9219 |
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+ | 0.2335 | 81.0 | 162 | 0.1977 | 0.9219 |
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+ | 0.2335 | 82.0 | 164 | 0.1741 | 0.9375 |
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+ | 0.2335 | 83.0 | 166 | 0.1468 | 0.9375 |
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+ | 0.2335 | 84.0 | 168 | 0.1355 | 0.9688 |
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+ | 0.0918 | 85.0 | 170 | 0.1447 | 0.9688 |
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+ | 0.0918 | 86.0 | 172 | 0.1628 | 0.9688 |
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+ | 0.0918 | 87.0 | 174 | 0.2077 | 0.9531 |
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+ | 0.0918 | 88.0 | 176 | 0.2623 | 0.9375 |
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+ | 0.0918 | 89.0 | 178 | 0.2854 | 0.9375 |
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+ | 0.0132 | 90.0 | 180 | 0.3076 | 0.9375 |
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+ | 0.0132 | 91.0 | 182 | 0.2989 | 0.9375 |
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+ | 0.0132 | 92.0 | 184 | 0.2839 | 0.9531 |
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+ | 0.0132 | 93.0 | 186 | 0.2756 | 0.9531 |
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+ | 0.0132 | 94.0 | 188 | 0.2669 | 0.9531 |
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+ | 0.0035 | 95.0 | 190 | 0.2414 | 0.9531 |
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+ | 0.0035 | 96.0 | 192 | 0.2353 | 0.9375 |
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+ | 0.0035 | 97.0 | 194 | 0.2482 | 0.9531 |
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+ | 0.0035 | 98.0 | 196 | 0.2578 | 0.9375 |
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+ | 0.0035 | 99.0 | 198 | 0.2755 | 0.9375 |
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+ | 0.0013 | 100.0 | 200 | 0.2956 | 0.9375 |
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+ | 0.0013 | 101.0 | 202 | 0.3133 | 0.9531 |
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+ | 0.0013 | 102.0 | 204 | 0.3293 | 0.9531 |
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+ | 0.0013 | 103.0 | 206 | 0.3417 | 0.9531 |
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+ | 0.0013 | 104.0 | 208 | 0.3510 | 0.9531 |
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+ | 0.0005 | 105.0 | 210 | 0.3616 | 0.9531 |
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+ | 0.0005 | 106.0 | 212 | 0.3694 | 0.9531 |
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+ | 0.0005 | 107.0 | 214 | 0.3754 | 0.9531 |
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+ | 0.0005 | 108.0 | 216 | 0.3806 | 0.9531 |
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+ | 0.0005 | 109.0 | 218 | 0.3850 | 0.9531 |
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+ | 0.0004 | 110.0 | 220 | 0.3890 | 0.9531 |
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+ | 0.0004 | 111.0 | 222 | 0.3924 | 0.9531 |
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+ | 0.0004 | 112.0 | 224 | 0.3956 | 0.9531 |
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+ | 0.0004 | 113.0 | 226 | 0.3986 | 0.9531 |
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+ | 0.0004 | 114.0 | 228 | 0.4011 | 0.9531 |
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+ | 0.0003 | 115.0 | 230 | 0.4034 | 0.9531 |
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+ | 0.0003 | 116.0 | 232 | 0.4056 | 0.9531 |
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+ | 0.0003 | 117.0 | 234 | 0.4076 | 0.9531 |
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+ | 0.0003 | 118.0 | 236 | 0.4118 | 0.9531 |
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+ | 0.0003 | 119.0 | 238 | 0.4199 | 0.9531 |
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+ | 0.0003 | 120.0 | 240 | 0.4298 | 0.9375 |
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+ | 0.0003 | 121.0 | 242 | 0.4401 | 0.9375 |
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+ | 0.0003 | 122.0 | 244 | 0.4495 | 0.9375 |
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+ | 0.0003 | 123.0 | 246 | 0.4602 | 0.9375 |
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+ | 0.0003 | 124.0 | 248 | 0.4687 | 0.9375 |
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+ | 0.0003 | 125.0 | 250 | 0.4755 | 0.9375 |
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+ | 0.0003 | 126.0 | 252 | 0.4813 | 0.9375 |
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+ | 0.0003 | 127.0 | 254 | 0.4855 | 0.9375 |
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+ | 0.0003 | 128.0 | 256 | 0.4896 | 0.9375 |
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+ | 0.0003 | 129.0 | 258 | 0.4940 | 0.9375 |
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+ | 0.0002 | 130.0 | 260 | 0.4967 | 0.9375 |
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+ | 0.0002 | 131.0 | 262 | 0.4963 | 0.9375 |
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+ | 0.0002 | 132.0 | 264 | 0.4903 | 0.9375 |
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+ | 0.0002 | 133.0 | 266 | 0.4861 | 0.9375 |
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+ | 0.0002 | 134.0 | 268 | 0.4831 | 0.9375 |
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+ | 0.0003 | 135.0 | 270 | 0.4804 | 0.9375 |
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+ | 0.0003 | 136.0 | 272 | 0.4780 | 0.9375 |
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+ | 0.0003 | 137.0 | 274 | 0.4761 | 0.9375 |
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+ | 0.0003 | 138.0 | 276 | 0.4721 | 0.9375 |
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+ | 0.0003 | 139.0 | 278 | 0.4686 | 0.9375 |
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+ | 0.0002 | 140.0 | 280 | 0.4646 | 0.9375 |
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+ | 0.0002 | 141.0 | 282 | 0.4593 | 0.9375 |
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+ | 0.0002 | 142.0 | 284 | 0.4542 | 0.9375 |
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+ | 0.0002 | 143.0 | 286 | 0.4495 | 0.9375 |
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+ | 0.0002 | 144.0 | 288 | 0.4472 | 0.9375 |
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+ | 0.0002 | 145.0 | 290 | 0.4465 | 0.9375 |
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+ | 0.0002 | 146.0 | 292 | 0.4467 | 0.9375 |
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+ | 0.0002 | 147.0 | 294 | 0.4469 | 0.9375 |
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+ | 0.0002 | 148.0 | 296 | 0.4474 | 0.9375 |
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+ | 0.0002 | 149.0 | 298 | 0.4483 | 0.9375 |
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+ | 0.0002 | 150.0 | 300 | 0.4497 | 0.9375 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.0.dev0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.4.0
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+ - Tokenizers 0.13.3