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--- |
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library_name: peft |
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license: mit |
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base_model: numind/NuExtract-v1.5 |
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tags: |
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- axolotl |
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- generated_from_trainer |
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model-index: |
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- name: e78cbcf5-a7ab-476b-b2ad-a344475d3d47 |
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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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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) |
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<br> |
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# e78cbcf5-a7ab-476b-b2ad-a344475d3d47 |
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This model is a fine-tuned version of [numind/NuExtract-v1.5](https://huggingface.co/numind/NuExtract-v1.5) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7836 |
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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: 0.00021 |
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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: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 50 |
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- training_steps: 500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| No log | 0.0001 | 1 | 1.9060 | |
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| 4.0056 | 0.0062 | 50 | 1.8324 | |
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| 3.799 | 0.0124 | 100 | 1.8217 | |
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| 3.9889 | 0.0186 | 150 | 1.8094 | |
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| 3.8805 | 0.0248 | 200 | 1.8063 | |
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| 3.9561 | 0.0309 | 250 | 1.8012 | |
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| 3.9048 | 0.0371 | 300 | 1.7920 | |
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| 3.9272 | 0.0433 | 350 | 1.7879 | |
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| 3.79 | 0.0495 | 400 | 1.7847 | |
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| 3.8634 | 0.0557 | 450 | 1.7835 | |
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| 3.8812 | 0.0619 | 500 | 1.7836 | |
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### Framework versions |
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- PEFT 0.13.2 |
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- Transformers 4.46.0 |
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- Pytorch 2.5.0+cu124 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
