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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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- f1 |
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model-index: |
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- name: distilbert_sa_GLUE_Experiment_data_aug_qqp_256 |
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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 QQP |
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type: glue |
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args: qqp |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7887954489240663 |
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- name: F1 |
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type: f1 |
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value: 0.7301115711621732 |
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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_sa_GLUE_Experiment_data_aug_qqp_256 |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5126 |
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- Accuracy: 0.7888 |
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- F1: 0.7301 |
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- Combined Score: 0.7595 |
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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 | F1 | Combined Score | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:| |
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| 0.3952 | 1.0 | 29671 | 0.5126 | 0.7888 | 0.7301 | 0.7595 | |
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| 0.2233 | 2.0 | 59342 | 0.5941 | 0.7960 | 0.7346 | 0.7653 | |
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| 0.147 | 3.0 | 89013 | 0.6603 | 0.7997 | 0.7340 | 0.7668 | |
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| 0.1067 | 4.0 | 118684 | 0.7091 | 0.8012 | 0.7376 | 0.7694 | |
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| 0.082 | 5.0 | 148355 | 0.8757 | 0.8000 | 0.7377 | 0.7688 | |
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| 0.0652 | 6.0 | 178026 | 0.8332 | 0.8044 | 0.7379 | 0.7711 | |
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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 2.9.0 |
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- Tokenizers 0.13.2 |
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