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roberta-base_stereoset_finetuned

Text Classification
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PyTorch
roberta
generated_from_trainer
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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - stereoset
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: roberta-base_stereoset_finetuned
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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: stereoset
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+ type: stereoset
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+ config: intersentence
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+ split: validation
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+ args: intersentence
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.7904238618524333
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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-base_stereoset_finetuned
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+
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+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the stereoset dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8461
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+ - Accuracy: 0.7904
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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: 5e-05
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+ - train_batch_size: 128
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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: 10
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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 | 0.21 | 5 | 0.6915 | 0.5149 |
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+ | No log | 0.42 | 10 | 0.6945 | 0.4914 |
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+ | No log | 0.62 | 15 | 0.6931 | 0.4945 |
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+ | No log | 0.83 | 20 | 0.6814 | 0.5086 |
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+ | No log | 1.04 | 25 | 0.6454 | 0.6978 |
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+ | No log | 1.25 | 30 | 0.5807 | 0.7088 |
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+ | No log | 1.46 | 35 | 0.5620 | 0.7284 |
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+ | No log | 1.67 | 40 | 0.5410 | 0.7331 |
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+ | No log | 1.88 | 45 | 0.4965 | 0.7630 |
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+ | No log | 2.08 | 50 | 0.4924 | 0.7614 |
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+ | No log | 2.29 | 55 | 0.4906 | 0.7661 |
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+ | No log | 2.5 | 60 | 0.5141 | 0.7661 |
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+ | No log | 2.71 | 65 | 0.4826 | 0.7700 |
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+ | No log | 2.92 | 70 | 0.4977 | 0.7630 |
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+ | No log | 3.12 | 75 | 0.4890 | 0.7802 |
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+ | No log | 3.33 | 80 | 0.4819 | 0.7857 |
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+ | No log | 3.54 | 85 | 0.4840 | 0.7834 |
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+ | No log | 3.75 | 90 | 0.5189 | 0.7794 |
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+ | No log | 3.96 | 95 | 0.5000 | 0.7912 |
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+ | No log | 4.17 | 100 | 0.4958 | 0.7865 |
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+ | No log | 4.38 | 105 | 0.5149 | 0.7896 |
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+ | No log | 4.58 | 110 | 0.5515 | 0.7975 |
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+ | No log | 4.79 | 115 | 0.5766 | 0.7873 |
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+ | No log | 5.0 | 120 | 0.5867 | 0.7873 |
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+ | No log | 5.21 | 125 | 0.6143 | 0.7936 |
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+ | No log | 5.42 | 130 | 0.6226 | 0.7881 |
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+ | No log | 5.62 | 135 | 0.6374 | 0.7865 |
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+ | No log | 5.83 | 140 | 0.6405 | 0.7983 |
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+ | No log | 6.04 | 145 | 0.6116 | 0.8006 |
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+ | No log | 6.25 | 150 | 0.6372 | 0.7983 |
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+ | No log | 6.46 | 155 | 0.6804 | 0.7881 |
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+ | No log | 6.67 | 160 | 0.7237 | 0.7857 |
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+ | No log | 6.88 | 165 | 0.7038 | 0.7904 |
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+ | No log | 7.08 | 170 | 0.7100 | 0.7991 |
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+ | No log | 7.29 | 175 | 0.6837 | 0.7920 |
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+ | No log | 7.5 | 180 | 0.7203 | 0.8046 |
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+ | No log | 7.71 | 185 | 0.7478 | 0.7959 |
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+ | No log | 7.92 | 190 | 0.7667 | 0.7920 |
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+ | No log | 8.12 | 195 | 0.7792 | 0.7959 |
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+ | No log | 8.33 | 200 | 0.8014 | 0.7943 |
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+ | No log | 8.54 | 205 | 0.8193 | 0.7959 |
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+ | No log | 8.75 | 210 | 0.8316 | 0.7967 |
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+ | No log | 8.96 | 215 | 0.8411 | 0.7896 |
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+ | No log | 9.17 | 220 | 0.8652 | 0.7936 |
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+ | No log | 9.38 | 225 | 0.8553 | 0.7841 |
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+ | No log | 9.58 | 230 | 0.8458 | 0.7881 |
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+ | No log | 9.79 | 235 | 0.8456 | 0.7912 |
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+ | No log | 10.0 | 240 | 0.8461 | 0.7904 |
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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 2.9.0
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+ - Tokenizers 0.13.2