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--- |
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language: |
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- en |
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tags: |
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- generated_from_trainer |
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datasets: |
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- glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: pixel-base-finetuned-qnli |
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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: GLUE QNLI |
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type: glue |
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args: qnli |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8859600951857953 |
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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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# pixel-base-finetuned-qnli |
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This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co./Team-PIXEL/pixel-base) on the GLUE QNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9503 |
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- Accuracy: 0.8860 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 8 |
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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: 100 |
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- training_steps: 15000 |
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- mixed_precision_training: Apex, opt level O1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.5451 | 0.31 | 500 | 0.5379 | 0.7282 | |
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| 0.4451 | 0.61 | 1000 | 0.3846 | 0.8318 | |
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| 0.4567 | 0.92 | 1500 | 0.3543 | 0.8525 | |
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| 0.3558 | 1.22 | 2000 | 0.3294 | 0.8638 | |
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| 0.3324 | 1.53 | 2500 | 0.3221 | 0.8666 | |
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| 0.3434 | 1.83 | 3000 | 0.2976 | 0.8774 | |
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| 0.2573 | 2.14 | 3500 | 0.3193 | 0.8750 | |
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| 0.2411 | 2.44 | 4000 | 0.3044 | 0.8794 | |
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| 0.253 | 2.75 | 4500 | 0.2932 | 0.8834 | |
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| 0.1653 | 3.05 | 5000 | 0.3364 | 0.8841 | |
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| 0.1662 | 3.36 | 5500 | 0.3348 | 0.8797 | |
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| 0.1816 | 3.67 | 6000 | 0.3440 | 0.8869 | |
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| 0.1699 | 3.97 | 6500 | 0.3453 | 0.8845 | |
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| 0.1027 | 4.28 | 7000 | 0.4277 | 0.8810 | |
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| 0.0987 | 4.58 | 7500 | 0.4590 | 0.8832 | |
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| 0.0974 | 4.89 | 8000 | 0.4311 | 0.8783 | |
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| 0.0669 | 5.19 | 8500 | 0.5214 | 0.8819 | |
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| 0.0583 | 5.5 | 9000 | 0.5776 | 0.8850 | |
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| 0.065 | 5.8 | 9500 | 0.5646 | 0.8821 | |
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| 0.0381 | 6.11 | 10000 | 0.6252 | 0.8796 | |
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| 0.0314 | 6.41 | 10500 | 0.7222 | 0.8801 | |
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| 0.0453 | 6.72 | 11000 | 0.6951 | 0.8823 | |
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| 0.0264 | 7.03 | 11500 | 0.7620 | 0.8828 | |
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| 0.0215 | 7.33 | 12000 | 0.8160 | 0.8834 | |
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| 0.0176 | 7.64 | 12500 | 0.8583 | 0.8828 | |
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| 0.0245 | 7.94 | 13000 | 0.8484 | 0.8867 | |
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| 0.0124 | 8.25 | 13500 | 0.8927 | 0.8836 | |
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| 0.0112 | 8.55 | 14000 | 0.9368 | 0.8827 | |
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| 0.0154 | 8.86 | 14500 | 0.9405 | 0.8860 | |
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| 0.0046 | 9.16 | 15000 | 0.9503 | 0.8860 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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