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--- |
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base_model: VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384 |
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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: checkpoints_10_1_microsoft_deberta_V1.1_384 |
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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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# checkpoints_10_1_microsoft_deberta_V1.1_384 |
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This model is a fine-tuned version of [VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384](https://huggingface.co/VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7675 |
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- Map@3: 0.8483 |
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- Accuracy: 0.755 |
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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: 2e-06 |
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- train_batch_size: 2 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 1200 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Map@3 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| |
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| 1.5583 | 0.05 | 100 | 1.4269 | 0.7675 | 0.65 | |
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| 1.1541 | 0.11 | 200 | 1.0863 | 0.765 | 0.66 | |
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| 1.0126 | 0.16 | 300 | 0.9547 | 0.8133 | 0.72 | |
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| 0.9608 | 0.21 | 400 | 0.8926 | 0.8275 | 0.74 | |
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| 0.9224 | 0.27 | 500 | 0.8429 | 0.8400 | 0.76 | |
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| 0.8834 | 0.32 | 600 | 0.8297 | 0.8342 | 0.745 | |
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| 0.8585 | 0.37 | 700 | 0.7904 | 0.8483 | 0.76 | |
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| 0.8491 | 0.43 | 800 | 0.7726 | 0.8542 | 0.765 | |
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| 0.878 | 0.48 | 900 | 0.7693 | 0.8517 | 0.755 | |
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| 0.8529 | 0.53 | 1000 | 0.7703 | 0.8450 | 0.75 | |
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| 0.8485 | 0.59 | 1100 | 0.7682 | 0.8483 | 0.755 | |
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| 0.8353 | 0.64 | 1200 | 0.7675 | 0.8483 | 0.755 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.3 |
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