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README.md
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---
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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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- spearmanr
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model-index:
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- name: mobilebert_add_GLUE_Experiment_stsb_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
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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: 0.060373490039481906
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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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# mobilebert_add_GLUE_Experiment_stsb_256
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This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.4995
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- Pearson: 0.0614
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- Spearmanr: 0.0604
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- Combined Score: 0.0609
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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: 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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| 4.3289 | 1.0 | 45 | 2.5185 | 0.0152 | 0.0186 | 0.0169 |
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| 2.1532 | 2.0 | 90 | 2.5569 | 0.0388 | 0.0235 | 0.0312 |
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| 2.1161 | 3.0 | 135 | 2.2965 | 0.0399 | 0.0217 | 0.0308 |
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| 2.1083 | 4.0 | 180 | 2.4191 | 0.0409 | 0.0284 | 0.0347 |
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| 2.0885 | 5.0 | 225 | 2.5610 | 0.0458 | 0.0382 | 0.0420 |
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| 2.0602 | 6.0 | 270 | 2.3788 | 0.0473 | 0.0444 | 0.0458 |
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| 2.0283 | 7.0 | 315 | 2.4459 | 0.0571 | 0.0527 | 0.0549 |
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| 1.9677 | 8.0 | 360 | 2.4995 | 0.0614 | 0.0604 | 0.0609 |
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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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