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--- |
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license: apache-2.0 |
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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: google/flan-t5-small |
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model-index: |
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- name: LoRA-FlanT5-small-v2 |
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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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# LoRA-FlanT5-small-v2 |
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co./google/flan-t5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1180 |
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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: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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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- num_epochs: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.2177 | 0.32 | 250 | 0.1693 | |
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| 0.1834 | 0.64 | 500 | 0.1321 | |
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| 0.1633 | 0.96 | 750 | 0.1227 | |
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| 0.1567 | 1.28 | 1000 | 0.1201 | |
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| 0.1533 | 1.61 | 1250 | 0.1189 | |
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| 0.1521 | 1.93 | 1500 | 0.1185 | |
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| 0.1531 | 2.25 | 1750 | 0.1182 | |
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| 0.1524 | 2.57 | 2000 | 0.1181 | |
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| 0.1523 | 2.89 | 2250 | 0.1181 | |
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| 0.1527 | 3.21 | 2500 | 0.1181 | |
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| 0.1527 | 3.53 | 2750 | 0.1180 | |
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| 0.152 | 3.85 | 3000 | 0.1181 | |
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| 0.1518 | 4.17 | 3250 | 0.1180 | |
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| 0.1504 | 4.49 | 3500 | 0.1180 | |
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| 0.1528 | 4.82 | 3750 | 0.1180 | |
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| 0.1511 | 5.14 | 4000 | 0.1180 | |
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| 0.1511 | 5.46 | 4250 | 0.1180 | |
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| 0.1517 | 5.78 | 4500 | 0.1180 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.15.2 |
