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simonycl
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bert-base-uncased-sst-2-16-87

Text Classification
Transformers
PyTorch
bert
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-2-16-13
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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-2-16-13
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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: 0.4022
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+ - Accuracy: 0.7812
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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 | 1 | 0.6926 | 0.5 |
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+ | No log | 2.0 | 2 | 0.6926 | 0.5 |
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+ | No log | 3.0 | 3 | 0.6926 | 0.5 |
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+ | No log | 4.0 | 4 | 0.6926 | 0.5 |
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+ | No log | 5.0 | 5 | 0.6926 | 0.5 |
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+ | No log | 6.0 | 6 | 0.6926 | 0.5 |
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+ | No log | 7.0 | 7 | 0.6925 | 0.5 |
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+ | No log | 8.0 | 8 | 0.6925 | 0.5 |
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+ | No log | 9.0 | 9 | 0.6925 | 0.5 |
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+ | 0.6898 | 10.0 | 10 | 0.6925 | 0.5 |
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+ | 0.6898 | 11.0 | 11 | 0.6924 | 0.5 |
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+ | 0.6898 | 12.0 | 12 | 0.6924 | 0.5 |
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+ | 0.6898 | 13.0 | 13 | 0.6924 | 0.5 |
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+ | 0.6898 | 14.0 | 14 | 0.6924 | 0.5 |
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+ | 0.6898 | 15.0 | 15 | 0.6923 | 0.5 |
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+ | 0.6898 | 16.0 | 16 | 0.6923 | 0.5 |
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+ | 0.6898 | 17.0 | 17 | 0.6922 | 0.5 |
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+ | 0.6898 | 18.0 | 18 | 0.6922 | 0.5 |
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+ | 0.6898 | 19.0 | 19 | 0.6922 | 0.5 |
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+ | 0.694 | 20.0 | 20 | 0.6921 | 0.5 |
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+ | 0.694 | 21.0 | 21 | 0.6921 | 0.5 |
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+ | 0.694 | 22.0 | 22 | 0.6920 | 0.5 |
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+ | 0.694 | 23.0 | 23 | 0.6920 | 0.5 |
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+ | 0.694 | 24.0 | 24 | 0.6920 | 0.5 |
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+ | 0.694 | 25.0 | 25 | 0.6919 | 0.5 |
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+ | 0.694 | 26.0 | 26 | 0.6919 | 0.5 |
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+ | 0.694 | 27.0 | 27 | 0.6918 | 0.5 |
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+ | 0.694 | 28.0 | 28 | 0.6918 | 0.5 |
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+ | 0.694 | 29.0 | 29 | 0.6918 | 0.5 |
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+ | 0.7021 | 30.0 | 30 | 0.6917 | 0.5 |
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+ | 0.7021 | 31.0 | 31 | 0.6916 | 0.5 |
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+ | 0.7021 | 32.0 | 32 | 0.6916 | 0.5 |
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+ | 0.7021 | 33.0 | 33 | 0.6916 | 0.5 |
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+ | 0.7021 | 34.0 | 34 | 0.6915 | 0.5 |
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+ | 0.7021 | 35.0 | 35 | 0.6915 | 0.5 |
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+ | 0.7021 | 36.0 | 36 | 0.6914 | 0.5 |
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+ | 0.7021 | 37.0 | 37 | 0.6914 | 0.5 |
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+ | 0.7021 | 38.0 | 38 | 0.6913 | 0.5 |
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+ | 0.7021 | 39.0 | 39 | 0.6913 | 0.5 |
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+ | 0.6798 | 40.0 | 40 | 0.6913 | 0.5 |
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+ | 0.6798 | 41.0 | 41 | 0.6912 | 0.5 |
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+ | 0.6798 | 42.0 | 42 | 0.6911 | 0.5 |
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+ | 0.6798 | 43.0 | 43 | 0.6910 | 0.5 |
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+ | 0.6798 | 44.0 | 44 | 0.6909 | 0.5 |
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+ | 0.6798 | 45.0 | 45 | 0.6908 | 0.5 |
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+ | 0.6798 | 46.0 | 46 | 0.6907 | 0.5 |
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+ | 0.6798 | 47.0 | 47 | 0.6906 | 0.5 |
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+ | 0.6798 | 48.0 | 48 | 0.6905 | 0.5 |
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+ | 0.6798 | 49.0 | 49 | 0.6903 | 0.5 |
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+ | 0.6874 | 50.0 | 50 | 0.6902 | 0.5 |
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+ | 0.6874 | 51.0 | 51 | 0.6901 | 0.5 |
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+ | 0.6874 | 52.0 | 52 | 0.6899 | 0.5 |
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+ | 0.6874 | 53.0 | 53 | 0.6898 | 0.5 |
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+ | 0.6874 | 54.0 | 54 | 0.6896 | 0.5 |
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+ | 0.6874 | 55.0 | 55 | 0.6895 | 0.5 |
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+ | 0.6874 | 56.0 | 56 | 0.6894 | 0.5 |
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+ | 0.6874 | 57.0 | 57 | 0.6893 | 0.5 |
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+ | 0.6874 | 58.0 | 58 | 0.6892 | 0.5 |
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+ | 0.6874 | 59.0 | 59 | 0.6890 | 0.5 |
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+ | 0.6878 | 60.0 | 60 | 0.6889 | 0.5 |
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+ | 0.6878 | 61.0 | 61 | 0.6888 | 0.5 |
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+ | 0.6878 | 62.0 | 62 | 0.6886 | 0.5 |
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+ | 0.6878 | 63.0 | 63 | 0.6885 | 0.5 |
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+ | 0.6878 | 64.0 | 64 | 0.6884 | 0.5 |
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+ | 0.6878 | 65.0 | 65 | 0.6884 | 0.5 |
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+ | 0.6878 | 66.0 | 66 | 0.6883 | 0.5 |
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+ | 0.6878 | 67.0 | 67 | 0.6882 | 0.5 |
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+ | 0.6878 | 68.0 | 68 | 0.6882 | 0.5 |
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+ | 0.6878 | 69.0 | 69 | 0.6881 | 0.5 |
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+ | 0.6805 | 70.0 | 70 | 0.6880 | 0.5312 |
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+ | 0.6805 | 71.0 | 71 | 0.6878 | 0.5312 |
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+ | 0.6805 | 72.0 | 72 | 0.6877 | 0.5312 |
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+ | 0.6805 | 73.0 | 73 | 0.6874 | 0.5312 |
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+ | 0.6805 | 74.0 | 74 | 0.6872 | 0.5312 |
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+ | 0.6805 | 75.0 | 75 | 0.6870 | 0.5312 |
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+ | 0.6805 | 76.0 | 76 | 0.6868 | 0.5312 |
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+ | 0.6805 | 77.0 | 77 | 0.6865 | 0.5312 |
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+ | 0.6805 | 78.0 | 78 | 0.6862 | 0.5 |
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+ | 0.6805 | 79.0 | 79 | 0.6860 | 0.5 |
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+ | 0.6675 | 80.0 | 80 | 0.6857 | 0.5 |
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+ | 0.6675 | 81.0 | 81 | 0.6853 | 0.5312 |
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+ | 0.6675 | 82.0 | 82 | 0.6849 | 0.5312 |
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+ | 0.6675 | 83.0 | 83 | 0.6845 | 0.5312 |
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+ | 0.6675 | 84.0 | 84 | 0.6840 | 0.5312 |
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+ | 0.6675 | 85.0 | 85 | 0.6834 | 0.5625 |
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+ | 0.6675 | 86.0 | 86 | 0.6827 | 0.5625 |
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+ | 0.6675 | 87.0 | 87 | 0.6818 | 0.5625 |
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+ | 0.6675 | 88.0 | 88 | 0.6809 | 0.5625 |
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+ | 0.6675 | 89.0 | 89 | 0.6798 | 0.5625 |
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+ | 0.65 | 90.0 | 90 | 0.6786 | 0.5625 |
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+ | 0.65 | 91.0 | 91 | 0.6772 | 0.5625 |
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+ | 0.65 | 92.0 | 92 | 0.6758 | 0.5625 |
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+ | 0.65 | 93.0 | 93 | 0.6741 | 0.5625 |
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+ | 0.65 | 94.0 | 94 | 0.6718 | 0.5625 |
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+ | 0.65 | 95.0 | 95 | 0.6687 | 0.5625 |
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+ | 0.65 | 96.0 | 96 | 0.6649 | 0.5625 |
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+ | 0.65 | 97.0 | 97 | 0.6615 | 0.5625 |
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+ | 0.65 | 98.0 | 98 | 0.6596 | 0.5625 |
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+ | 0.65 | 99.0 | 99 | 0.6605 | 0.5625 |
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+ | 0.611 | 100.0 | 100 | 0.6642 | 0.5625 |
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+ | 0.611 | 101.0 | 101 | 0.6683 | 0.5625 |
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+ | 0.611 | 102.0 | 102 | 0.6689 | 0.5625 |
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+ | 0.611 | 103.0 | 103 | 0.6670 | 0.5625 |
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+ | 0.611 | 104.0 | 104 | 0.6627 | 0.5312 |
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+ | 0.611 | 105.0 | 105 | 0.6595 | 0.5312 |
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+ | 0.611 | 106.0 | 106 | 0.6577 | 0.5625 |
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+ | 0.611 | 107.0 | 107 | 0.6575 | 0.5938 |
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+ | 0.611 | 108.0 | 108 | 0.6552 | 0.5938 |
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+ | 0.611 | 109.0 | 109 | 0.6555 | 0.625 |
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+ | 0.5787 | 110.0 | 110 | 0.6560 | 0.625 |
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+ | 0.5787 | 111.0 | 111 | 0.6566 | 0.625 |
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+ | 0.5787 | 112.0 | 112 | 0.6560 | 0.625 |
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+ | 0.5787 | 113.0 | 113 | 0.6543 | 0.6562 |
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+ | 0.5787 | 114.0 | 114 | 0.6530 | 0.6562 |
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+ | 0.5787 | 115.0 | 115 | 0.6518 | 0.6562 |
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+ | 0.5787 | 116.0 | 116 | 0.6512 | 0.6562 |
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+ | 0.5787 | 117.0 | 117 | 0.6506 | 0.6562 |
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+ | 0.5787 | 118.0 | 118 | 0.6500 | 0.6562 |
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+ | 0.5787 | 119.0 | 119 | 0.6499 | 0.6875 |
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+ | 0.5279 | 120.0 | 120 | 0.6497 | 0.6875 |
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+ | 0.5279 | 121.0 | 121 | 0.6496 | 0.6875 |
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+ | 0.5279 | 122.0 | 122 | 0.6494 | 0.6875 |
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+ | 0.5279 | 123.0 | 123 | 0.6486 | 0.6875 |
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+ | 0.5279 | 124.0 | 124 | 0.6472 | 0.6875 |
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+ | 0.5279 | 125.0 | 125 | 0.6443 | 0.6875 |
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+ | 0.5279 | 126.0 | 126 | 0.6397 | 0.6562 |
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+ | 0.5279 | 127.0 | 127 | 0.6328 | 0.6562 |
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+ | 0.5279 | 128.0 | 128 | 0.6238 | 0.6875 |
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+ | 0.5279 | 129.0 | 129 | 0.6173 | 0.6875 |
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+ | 0.4721 | 130.0 | 130 | 0.6138 | 0.6875 |
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+ | 0.4721 | 131.0 | 131 | 0.6175 | 0.625 |
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+ | 0.4721 | 132.0 | 132 | 0.6137 | 0.6562 |
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+ | 0.4721 | 133.0 | 133 | 0.6101 | 0.6562 |
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+ | 0.4721 | 134.0 | 134 | 0.6062 | 0.6562 |
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+ | 0.4721 | 135.0 | 135 | 0.6027 | 0.6562 |
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+ | 0.4721 | 136.0 | 136 | 0.6015 | 0.625 |
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+ | 0.4721 | 137.0 | 137 | 0.5982 | 0.625 |
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+ | 0.4721 | 138.0 | 138 | 0.6102 | 0.625 |
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+ | 0.4721 | 139.0 | 139 | 0.5983 | 0.625 |
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+ | 0.378 | 140.0 | 140 | 0.6020 | 0.625 |
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+ | 0.378 | 141.0 | 141 | 0.5921 | 0.625 |
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+ | 0.378 | 142.0 | 142 | 0.5790 | 0.625 |
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+ | 0.378 | 143.0 | 143 | 0.5654 | 0.6562 |
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+ | 0.378 | 144.0 | 144 | 0.5493 | 0.6562 |
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+ | 0.378 | 145.0 | 145 | 0.5279 | 0.6562 |
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+ | 0.378 | 146.0 | 146 | 0.5064 | 0.6562 |
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+ | 0.378 | 147.0 | 147 | 0.4834 | 0.6875 |
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+ | 0.378 | 148.0 | 148 | 0.4557 | 0.7188 |
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+ | 0.378 | 149.0 | 149 | 0.4318 | 0.75 |
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+ | 0.2537 | 150.0 | 150 | 0.4022 | 0.7812 |
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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