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--- |
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license: mit |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: roberta-base |
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model-index: |
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- name: roberta-base_PrefixTuning_lr0.0005_bs4_epoch20_wd0.01 |
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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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# roberta-base_PrefixTuning_lr0.0005_bs4_epoch20_wd0.01 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.9612 |
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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: 0.0005 |
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- train_batch_size: 4 |
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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_steps: 500 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 16.1454 | 1.0 | 157 | 20.9147 | |
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| 14.0712 | 2.0 | 314 | 19.1650 | |
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| 11.1417 | 3.0 | 471 | 9.2979 | |
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| 6.9892 | 4.0 | 628 | 6.3310 | |
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| 6.4327 | 5.0 | 785 | 5.8392 | |
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| 5.9068 | 6.0 | 942 | 5.5211 | |
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| 5.7042 | 7.0 | 1099 | 5.2767 | |
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| 5.399 | 8.0 | 1256 | 5.0556 | |
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| 5.117 | 9.0 | 1413 | 4.8469 | |
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| 5.0361 | 10.0 | 1570 | 4.6365 | |
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| 4.8558 | 11.0 | 1727 | 4.4569 | |
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| 4.6969 | 12.0 | 1884 | 4.3400 | |
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| 4.5535 | 13.0 | 2041 | 4.2330 | |
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| 4.522 | 14.0 | 2198 | 4.1549 | |
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| 4.3703 | 15.0 | 2355 | 4.1031 | |
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| 4.305 | 16.0 | 2512 | 4.0450 | |
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| 4.3117 | 17.0 | 2669 | 4.0057 | |
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| 4.2937 | 18.0 | 2826 | 3.9820 | |
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| 4.2778 | 19.0 | 2983 | 3.9654 | |
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| 4.2304 | 20.0 | 3140 | 3.9612 | |
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### Framework versions |
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- PEFT 0.7.1 |
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- Transformers 4.36.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
