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--- |
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license: other |
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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: facebook/opt-350m |
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model-index: |
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- name: opt-350m_adalora_lr5e-05_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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# opt-350m_adalora_lr5e-05_bs4_epoch20_wd0.01 |
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This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4742 |
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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: 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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| 5.0651 | 1.0 | 157 | 4.7989 | |
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| 4.8493 | 2.0 | 314 | 4.5267 | |
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| 4.576 | 3.0 | 471 | 3.9509 | |
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| 3.8391 | 4.0 | 628 | 3.6083 | |
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| 3.676 | 5.0 | 785 | 3.5374 | |
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| 3.5459 | 6.0 | 942 | 3.5018 | |
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| 3.5045 | 7.0 | 1099 | 3.4823 | |
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| 3.4229 | 8.0 | 1256 | 3.4745 | |
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| 3.4334 | 9.0 | 1413 | 3.4691 | |
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| 3.3878 | 10.0 | 1570 | 3.4691 | |
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| 3.316 | 11.0 | 1727 | 3.4681 | |
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| 3.2654 | 12.0 | 1884 | 3.4660 | |
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| 3.2361 | 13.0 | 2041 | 3.4672 | |
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| 3.2146 | 14.0 | 2198 | 3.4672 | |
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| 3.2086 | 15.0 | 2355 | 3.4711 | |
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| 3.1561 | 16.0 | 2512 | 3.4700 | |
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| 3.163 | 17.0 | 2669 | 3.4721 | |
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| 3.1533 | 18.0 | 2826 | 3.4729 | |
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| 3.2128 | 19.0 | 2983 | 3.4742 | |
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| 3.1356 | 20.0 | 3140 | 3.4742 | |
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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 |
