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--- |
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language: |
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- en |
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license: apache-2.0 |
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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: distilbert_add_GLUE_Experiment_qnli_192 |
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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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config: qnli |
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split: validation |
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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: 0.594911220940875 |
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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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# distilbert_add_GLUE_Experiment_qnli_192 |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6649 |
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- Accuracy: 0.5949 |
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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: 5e-05 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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- seed: 10 |
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- distributed_type: multi-GPU |
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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: 50 |
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- mixed_precision_training: Native AMP |
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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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| 0.6936 | 1.0 | 410 | 0.6930 | 0.5054 | |
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| 0.6793 | 2.0 | 820 | 0.6684 | 0.5823 | |
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| 0.6511 | 3.0 | 1230 | 0.6650 | 0.5938 | |
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| 0.6385 | 4.0 | 1640 | 0.6649 | 0.5949 | |
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| 0.6306 | 5.0 | 2050 | 0.6668 | 0.5923 | |
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| 0.6215 | 6.0 | 2460 | 0.6783 | 0.5931 | |
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| 0.6137 | 7.0 | 2870 | 0.6969 | 0.5852 | |
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| 0.6046 | 8.0 | 3280 | 0.6888 | 0.5881 | |
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| 0.5964 | 9.0 | 3690 | 0.6977 | 0.5799 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.14.0a0+410ce96 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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