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xlnet-base-cased_stereoset_finetuned

Text Classification
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PyTorch
xlnet
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: xlnet-base-cased_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: 0.7441130298273155
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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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+ # xlnet-base-cased_stereoset_finetuned
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+
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+ This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the stereoset dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0332
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+ - Accuracy: 0.7441
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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 | 0.7165 | 0.5055 |
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+ | No log | 0.42 | 10 | 0.6932 | 0.5 |
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+ | No log | 0.62 | 15 | 0.6971 | 0.5047 |
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+ | No log | 0.83 | 20 | 0.7107 | 0.4953 |
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+ | No log | 1.04 | 25 | 0.6895 | 0.5047 |
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+ | No log | 1.25 | 30 | 0.6715 | 0.5840 |
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+ | No log | 1.46 | 35 | 0.6476 | 0.6476 |
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+ | No log | 1.67 | 40 | 0.6150 | 0.6970 |
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+ | No log | 1.88 | 45 | 0.6170 | 0.6884 |
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+ | No log | 2.08 | 50 | 0.6065 | 0.6797 |
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+ | No log | 2.29 | 55 | 0.5865 | 0.7033 |
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+ | No log | 2.5 | 60 | 0.5899 | 0.7064 |
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+ | No log | 2.71 | 65 | 0.5980 | 0.7151 |
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+ | No log | 2.92 | 70 | 0.5890 | 0.7229 |
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+ | No log | 3.12 | 75 | 0.5930 | 0.7190 |
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+ | No log | 3.33 | 80 | 0.6430 | 0.7049 |
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+ | No log | 3.54 | 85 | 0.6677 | 0.7198 |
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+ | No log | 3.75 | 90 | 0.6076 | 0.7370 |
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+ | No log | 3.96 | 95 | 0.6041 | 0.7339 |
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+ | No log | 4.17 | 100 | 0.6324 | 0.7323 |
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+ | No log | 4.38 | 105 | 0.6990 | 0.7308 |
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+ | No log | 4.58 | 110 | 0.7081 | 0.7433 |
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+ | No log | 4.79 | 115 | 0.6549 | 0.7237 |
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+ | No log | 5.0 | 120 | 0.6868 | 0.7072 |
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+ | No log | 5.21 | 125 | 0.6525 | 0.7363 |
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+ | No log | 5.42 | 130 | 0.7622 | 0.7418 |
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+ | No log | 5.62 | 135 | 0.7730 | 0.7402 |
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+ | No log | 5.83 | 140 | 0.7788 | 0.7449 |
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+ | No log | 6.04 | 145 | 0.7609 | 0.7347 |
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+ | No log | 6.25 | 150 | 0.8058 | 0.7323 |
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+ | No log | 6.46 | 155 | 0.8525 | 0.7331 |
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+ | No log | 6.67 | 160 | 0.8504 | 0.7339 |
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+ | No log | 6.88 | 165 | 0.8424 | 0.7300 |
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+ | No log | 7.08 | 170 | 0.8413 | 0.7394 |
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+ | No log | 7.29 | 175 | 0.8808 | 0.7268 |
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+ | No log | 7.5 | 180 | 0.9058 | 0.7292 |
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+ | No log | 7.71 | 185 | 0.9338 | 0.7363 |
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+ | No log | 7.92 | 190 | 0.9412 | 0.7370 |
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+ | No log | 8.12 | 195 | 0.9453 | 0.7339 |
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+ | No log | 8.33 | 200 | 0.9544 | 0.7394 |
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+ | No log | 8.54 | 205 | 0.9664 | 0.7402 |
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+ | No log | 8.75 | 210 | 0.9840 | 0.7339 |
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+ | No log | 8.96 | 215 | 0.9896 | 0.7370 |
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+ | No log | 9.17 | 220 | 1.0239 | 0.7410 |
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+ | No log | 9.38 | 225 | 1.0306 | 0.7418 |
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+ | No log | 9.58 | 230 | 1.0358 | 0.7402 |
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+ | No log | 9.79 | 235 | 1.0351 | 0.7410 |
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+ | No log | 10.0 | 240 | 1.0332 | 0.7441 |
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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