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README.md
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---
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library_name: peft
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: plateer_classifier_test
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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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# plateer_classifier_test
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This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on [x2bee/plateer_category_data](https://huggingface.co/datasets/x2bee/plateer_category_data).
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It achieves the following results on the evaluation set:
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- [MLflow Result(https://polar-mlflow.x2bee.com/#/experiments/27/runs/baa7269894b14f91b8a8ea3822474476)]
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- Loss: 0.3242
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- Accuracy: 0.8997
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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: 0.0002
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- total_eval_batch_size: 32
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 10000
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- num_epochs: 1
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:------:|:------:|:---------------:|:--------:|
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| 0.5023 | 0.0292 | 5000 | 0.5044 | 0.8572 |
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| 0.4629 | 0.0585 | 10000 | 0.4571 | 0.8688 |
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| 0.4254 | 0.0878 | 15000 | 0.4201 | 0.8770 |
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| 0.4025 | 0.1171 | 20000 | 0.4016 | 0.8823 |
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| 0.3635 | 0.3220 | 55000 | 0.3623 | 0.8905 |
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| 0.3192 | 0.6441 | 110000 | 0.3242 | 0.8997 |
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### Framework versions
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- PEFT 0.13.2
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- Transformers 4.46.3
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- Pytorch 2.2.1
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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