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--- |
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language: |
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- en |
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license: mit |
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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: roberta-base-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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args: stsb |
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metrics: |
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- name: Spearmanr |
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type: spearmanr |
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value: 0.9092158650855444 |
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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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# roberta-base-stsb |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE STSB dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4221 |
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- Pearson: 0.9116 |
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- Spearmanr: 0.9092 |
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- Combined Score: 0.9104 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- lr_scheduler_warmup_ratio: 0.06 |
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- num_epochs: 10.0 |
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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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| 1.6552 | 1.39 | 500 | 0.5265 | 0.8925 | 0.8925 | 0.8925 | |
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| 0.3579 | 2.78 | 1000 | 0.4626 | 0.9022 | 0.8991 | 0.9007 | |
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| 0.2198 | 4.17 | 1500 | 0.4396 | 0.9054 | 0.9042 | 0.9048 | |
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| 0.1585 | 5.56 | 2000 | 0.4537 | 0.9069 | 0.9052 | 0.9060 | |
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| 0.1139 | 6.94 | 2500 | 0.4975 | 0.9091 | 0.9065 | 0.9078 | |
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| 0.0868 | 8.33 | 3000 | 0.4221 | 0.9116 | 0.9092 | 0.9104 | |
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| 0.073 | 9.72 | 3500 | 0.4311 | 0.9096 | 0.9077 | 0.9086 | |
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### Framework versions |
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- Transformers 4.21.3 |
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- Pytorch 1.7.1 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.6 |
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