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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: 0.907904999413384 |
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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: 0.4155 |
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- Pearson: 0.9101 |
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- Spearmanr: 0.9079 |
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- Combined Score: 0.9090 |
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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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| No log | 1.0 | 360 | 0.6202 | 0.8787 | 0.8813 | 0.8800 | |
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| 1.6425 | 2.0 | 720 | 0.4864 | 0.9008 | 0.8992 | 0.9000 | |
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| 0.3629 | 3.0 | 1080 | 0.4201 | 0.9043 | 0.9016 | 0.9030 | |
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| 0.3629 | 4.0 | 1440 | 0.4686 | 0.9052 | 0.9003 | 0.9027 | |
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| 0.2212 | 5.0 | 1800 | 0.4622 | 0.9061 | 0.9031 | 0.9046 | |
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| 0.1556 | 6.0 | 2160 | 0.3952 | 0.9086 | 0.9065 | 0.9075 | |
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| 0.1162 | 7.0 | 2520 | 0.4271 | 0.9081 | 0.9070 | 0.9075 | |
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| 0.1162 | 8.0 | 2880 | 0.4169 | 0.9094 | 0.9075 | 0.9085 | |
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| 0.0887 | 9.0 | 3240 | 0.4383 | 0.9091 | 0.9074 | 0.9083 | |
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| 0.0717 | 10.0 | 3600 | 0.4155 | 0.9101 | 0.9079 | 0.9090 | |
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### Framework versions |
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- Transformers 4.20.0.dev0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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