Model save
Browse files- README.md +70 -0
- pytorch_model.bin +1 -1
README.md
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---
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base_model: ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3
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tags:
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- generated_from_trainer
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datasets:
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- klue
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model-index:
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- name: sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep9_ckpt
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results: []
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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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# sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep9_ckpt
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This model is a fine-tuned version of [ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3](https://huggingface.co/ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3) on the klue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3250
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- Mse: 0.3250
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- Mae: 0.4166
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- R2: 0.8512
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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: 1e-05
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- train_batch_size: 64
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- eval_batch_size: 64
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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: 0.1
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- num_epochs: 9
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
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| 1.2084 | 1.0 | 183 | 0.5071 | 0.5071 | 0.5306 | 0.7678 |
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| 0.1515 | 2.0 | 366 | 0.3142 | 0.3142 | 0.4149 | 0.8561 |
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| 0.103 | 3.0 | 549 | 0.3284 | 0.3284 | 0.4150 | 0.8496 |
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| 0.0779 | 4.0 | 732 | 0.3306 | 0.3306 | 0.4184 | 0.8486 |
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| 0.0597 | 5.0 | 915 | 0.3219 | 0.3219 | 0.4098 | 0.8526 |
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| 0.0497 | 6.0 | 1098 | 0.3324 | 0.3324 | 0.4175 | 0.8478 |
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| 0.0407 | 7.0 | 1281 | 0.3114 | 0.3114 | 0.4119 | 0.8574 |
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| 0.0356 | 8.0 | 1464 | 0.3305 | 0.3305 | 0.4199 | 0.8486 |
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| 0.0327 | 9.0 | 1647 | 0.3250 | 0.3250 | 0.4166 | 0.8512 |
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### Framework versions
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- Transformers 4.33.2
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- Pytorch 2.0.1+cu117
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- Datasets 2.13.0
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- Tokenizers 0.13.3
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pytorch_model.bin
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