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README.md ADDED
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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_30_9_microsoft_deberta_V1.0_384
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+ results: []
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+ ---
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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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+
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+ # checkpoints_30_9_microsoft_deberta_V1.0_384
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+
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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: 1.5746
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+ - Map@3: 0.7625
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+ - Accuracy: 0.655
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-07
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Map@3 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
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+ | 1.6081 | 0.05 | 100 | 1.6083 | 0.7092 | 0.585 |
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+ | 1.6107 | 0.11 | 200 | 1.6078 | 0.7375 | 0.625 |
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+ | 1.6077 | 0.16 | 300 | 1.6070 | 0.7517 | 0.65 |
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+ | 1.6097 | 0.21 | 400 | 1.6055 | 0.7542 | 0.645 |
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+ | 1.6083 | 0.27 | 500 | 1.6030 | 0.7650 | 0.65 |
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+ | 1.6006 | 0.32 | 600 | 1.5989 | 0.7733 | 0.665 |
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+ | 1.5932 | 0.37 | 700 | 1.5927 | 0.7742 | 0.66 |
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+ | 1.5881 | 0.43 | 800 | 1.5858 | 0.7742 | 0.665 |
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+ | 1.578 | 0.48 | 900 | 1.5800 | 0.7708 | 0.66 |
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+ | 1.5717 | 0.53 | 1000 | 1.5763 | 0.7658 | 0.655 |
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+ | 1.5677 | 0.59 | 1100 | 1.5748 | 0.7625 | 0.655 |
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+ | 1.5666 | 0.64 | 1200 | 1.5746 | 0.7625 | 0.655 |
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+
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+
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+ ### Framework versions
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+
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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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