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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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- spearmanr |
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model-index: |
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- name: hBERTv2_new_pretrain_48_stsb |
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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 STSB |
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type: glue |
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config: stsb |
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split: validation |
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args: stsb |
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metrics: |
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- name: Spearmanr |
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type: spearmanr |
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value: 0.4028161409951644 |
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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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# hBERTv2_new_pretrain_48_stsb |
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This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_48) on the GLUE STSB dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0734 |
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- Pearson: 0.4184 |
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- Spearmanr: 0.4028 |
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- Combined Score: 0.4106 |
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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: 4e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| |
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| 2.2864 | 1.0 | 45 | 3.0157 | 0.1270 | 0.1171 | 0.1220 | |
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| 1.9895 | 2.0 | 90 | 2.7270 | 0.1553 | 0.1550 | 0.1552 | |
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| 1.7101 | 3.0 | 135 | 2.8223 | 0.2806 | 0.2657 | 0.2732 | |
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| 1.2973 | 4.0 | 180 | 2.5938 | 0.3375 | 0.3280 | 0.3328 | |
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| 1.0658 | 5.0 | 225 | 2.3835 | 0.3771 | 0.3629 | 0.3700 | |
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| 0.8454 | 6.0 | 270 | 2.5028 | 0.3637 | 0.3479 | 0.3558 | |
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| 0.6773 | 7.0 | 315 | 2.3937 | 0.3594 | 0.3538 | 0.3566 | |
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| 0.5678 | 8.0 | 360 | 2.6813 | 0.3803 | 0.3802 | 0.3803 | |
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| 0.4746 | 9.0 | 405 | 2.5546 | 0.3874 | 0.3695 | 0.3784 | |
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| 0.4113 | 10.0 | 450 | 2.2077 | 0.4112 | 0.4038 | 0.4075 | |
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| 0.3585 | 11.0 | 495 | 2.2846 | 0.4096 | 0.3972 | 0.4034 | |
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| 0.3288 | 12.0 | 540 | 2.4155 | 0.4012 | 0.3848 | 0.3930 | |
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| 0.2745 | 13.0 | 585 | 2.3635 | 0.4004 | 0.3924 | 0.3964 | |
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| 0.2579 | 14.0 | 630 | 2.0734 | 0.4184 | 0.4028 | 0.4106 | |
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| 0.2309 | 15.0 | 675 | 2.3462 | 0.4171 | 0.4026 | 0.4099 | |
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| 0.2037 | 16.0 | 720 | 2.2598 | 0.4225 | 0.4090 | 0.4157 | |
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| 0.1806 | 17.0 | 765 | 2.2458 | 0.4116 | 0.3916 | 0.4016 | |
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| 0.1785 | 18.0 | 810 | 2.3296 | 0.4088 | 0.3903 | 0.3996 | |
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| 0.1582 | 19.0 | 855 | 2.3369 | 0.4033 | 0.3868 | 0.3951 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 1.14.0a0+410ce96 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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