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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_sa_GLUE_Experiment_logit_kd_pretrain_mnli |
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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 MNLI |
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type: glue |
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config: mnli |
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split: validation_matched |
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args: mnli |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8105166802278275 |
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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_logit_kd_pretrain_mnli |
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This model is a fine-tuned version of [gokuls/distilbert_sa_pre-training-complete](https://huggingface.co/gokuls/distilbert_sa_pre-training-complete) on the GLUE MNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3863 |
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- Accuracy: 0.8105 |
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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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| 0.4379 | 1.0 | 1534 | 0.3984 | 0.7976 | |
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| 0.3845 | 2.0 | 3068 | 0.3953 | 0.8047 | |
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| 0.359 | 3.0 | 4602 | 0.3935 | 0.8102 | |
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| 0.3411 | 4.0 | 6136 | 0.3962 | 0.8077 | |
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| 0.3279 | 5.0 | 7670 | 0.3959 | 0.8172 | |
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| 0.3189 | 6.0 | 9204 | 0.4018 | 0.8102 | |
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| 0.3119 | 7.0 | 10738 | 0.4040 | 0.8073 | |
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| 0.3071 | 8.0 | 12272 | 0.3990 | 0.8175 | |
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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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