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deberta-v3-large_boolq

Text Classification
Transformers
PyTorch
Safetensors
deberta-v2
generated_from_trainer
Eval Results
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  1. README.md +9 -9
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.882262996941896
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the boolq dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5796
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- - Accuracy: 0.8823
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 1e-05
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  - train_batch_size: 16
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- - eval_batch_size: 64
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  - seed: 42
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  - gradient_accumulation_steps: 2
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  - total_train_batch_size: 32
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 0.85 | 250 | 0.3265 | 0.8740 |
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- | 0.3786 | 1.69 | 500 | 0.3212 | 0.8844 |
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- | 0.3786 | 2.54 | 750 | 0.4205 | 0.8838 |
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- | 0.1324 | 3.39 | 1000 | 0.5393 | 0.8832 |
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- | 0.1324 | 4.24 | 1250 | 0.5796 | 0.8823 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8837920489296636
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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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  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the boolq dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6847
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+ - Accuracy: 0.8838
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 1e-05
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  - train_batch_size: 16
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+ - eval_batch_size: 8
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  - seed: 42
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  - gradient_accumulation_steps: 2
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  - total_train_batch_size: 32
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.85 | 250 | 0.5306 | 0.8823 |
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+ | 0.1151 | 1.69 | 500 | 0.4601 | 0.8835 |
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+ | 0.1151 | 2.54 | 750 | 0.5897 | 0.8792 |
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+ | 0.0656 | 3.39 | 1000 | 0.6477 | 0.8804 |
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+ | 0.0656 | 4.24 | 1250 | 0.6847 | 0.8838 |
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  ### Framework versions
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