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roberta-large_stereoset_finetuned

Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Eval Results
Inference Endpoints
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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-large_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: 0.8335949764521193
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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_stereoset_finetuned
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+
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+ This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the stereoset dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7989
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+ - Accuracy: 0.8336
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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 | 0.6920 | 0.5196 |
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+ | No log | 0.42 | 10 | 0.6909 | 0.5290 |
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+ | No log | 0.62 | 15 | 0.6899 | 0.5220 |
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+ | No log | 0.83 | 20 | 0.6883 | 0.5408 |
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+ | No log | 1.04 | 25 | 0.6573 | 0.6609 |
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+ | No log | 1.25 | 30 | 0.5892 | 0.7088 |
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+ | No log | 1.46 | 35 | 0.6633 | 0.5408 |
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+ | No log | 1.67 | 40 | 0.6322 | 0.6852 |
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+ | No log | 1.88 | 45 | 0.6393 | 0.7159 |
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+ | No log | 2.08 | 50 | 0.5494 | 0.7410 |
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+ | No log | 2.29 | 55 | 0.5498 | 0.7386 |
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+ | No log | 2.5 | 60 | 0.5069 | 0.7692 |
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+ | No log | 2.71 | 65 | 0.4930 | 0.7630 |
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+ | No log | 2.92 | 70 | 0.4939 | 0.7614 |
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+ | No log | 3.12 | 75 | 0.5379 | 0.7724 |
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+ | No log | 3.33 | 80 | 0.5981 | 0.7732 |
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+ | No log | 3.54 | 85 | 0.5842 | 0.7716 |
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+ | No log | 3.75 | 90 | 0.4405 | 0.8030 |
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+ | No log | 3.96 | 95 | 0.4970 | 0.7951 |
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+ | No log | 4.17 | 100 | 0.5172 | 0.8093 |
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+ | No log | 4.38 | 105 | 0.5052 | 0.8108 |
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+ | No log | 4.58 | 110 | 0.4685 | 0.8085 |
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+ | No log | 4.79 | 115 | 0.4663 | 0.8218 |
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+ | No log | 5.0 | 120 | 0.5086 | 0.8218 |
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+ | No log | 5.21 | 125 | 0.5096 | 0.8179 |
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+ | No log | 5.42 | 130 | 0.5705 | 0.8203 |
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+ | No log | 5.62 | 135 | 0.5294 | 0.8312 |
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+ | No log | 5.83 | 140 | 0.4377 | 0.8375 |
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+ | No log | 6.04 | 145 | 0.5699 | 0.8100 |
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+ | No log | 6.25 | 150 | 0.6062 | 0.8265 |
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+ | No log | 6.46 | 155 | 0.7237 | 0.8218 |
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+ | No log | 6.67 | 160 | 0.6816 | 0.8210 |
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+ | No log | 6.88 | 165 | 0.6413 | 0.8124 |
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+ | No log | 7.08 | 170 | 0.5931 | 0.8359 |
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+ | No log | 7.29 | 175 | 0.6149 | 0.8399 |
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+ | No log | 7.5 | 180 | 0.7190 | 0.8195 |
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+ | No log | 7.71 | 185 | 0.7339 | 0.8352 |
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+ | No log | 7.92 | 190 | 0.7244 | 0.8352 |
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+ | No log | 8.12 | 195 | 0.7722 | 0.8203 |
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+ | No log | 8.33 | 200 | 0.6890 | 0.8344 |
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+ | No log | 8.54 | 205 | 0.6938 | 0.8336 |
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+ | No log | 8.75 | 210 | 0.7234 | 0.8320 |
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+ | No log | 8.96 | 215 | 0.7517 | 0.8391 |
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+ | No log | 9.17 | 220 | 0.7713 | 0.8383 |
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+ | No log | 9.38 | 225 | 0.7745 | 0.8375 |
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+ | No log | 9.58 | 230 | 0.8006 | 0.8375 |
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+ | No log | 9.79 | 235 | 0.8003 | 0.8367 |
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+ | No log | 10.0 | 240 | 0.7989 | 0.8336 |
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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