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strongpear
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deberta-large_vMCQ

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deberta-v2
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  1. README.md +16 -10
  2. model.safetensors +1 -1
README.md CHANGED
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  ---
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  license: cc-by-4.0
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- base_model: deepset/deberta-v3-large-squad2
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -15,10 +15,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # deberta-large_vMCQ
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- This model is a fine-tuned version of [deepset/deberta-v3-large-squad2](https://huggingface.co/deepset/deberta-v3-large-squad2) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3480
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- - Accuracy: 0.9061
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  ## Model description
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@@ -53,12 +53,18 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.5525 | 0.17 | 1000 | 0.4781 | 0.8777 |
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- | 0.4107 | 0.33 | 2000 | 0.3763 | 0.8924 |
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- | 0.3733 | 0.5 | 3000 | 0.3605 | 0.8967 |
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- | 0.39 | 0.67 | 4000 | 0.3378 | 0.9050 |
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- | 0.3674 | 0.83 | 5000 | 0.3407 | 0.9053 |
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- | 0.3507 | 1.0 | 6000 | 0.3480 | 0.9061 |
 
 
 
 
 
 
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  ### Framework versions
 
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  ---
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  license: cc-by-4.0
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+ base_model: strongpear/deberta-large_vMCQ
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  # deberta-large_vMCQ
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+ This model is a fine-tuned version of [strongpear/deberta-large_vMCQ](https://huggingface.co/strongpear/deberta-large_vMCQ) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3767
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+ - Accuracy: 0.9097
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.3415 | 0.08 | 500 | 0.3459 | 0.9033 |
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+ | 0.299 | 0.17 | 1000 | 0.5000 | 0.8988 |
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+ | 0.3761 | 0.25 | 1500 | 0.4322 | 0.8994 |
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+ | 0.3655 | 0.33 | 2000 | 0.4337 | 0.8990 |
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+ | 0.3576 | 0.42 | 2500 | 0.4655 | 0.9027 |
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+ | 0.3178 | 0.5 | 3000 | 0.4377 | 0.9046 |
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+ | 0.3789 | 0.58 | 3500 | 0.4241 | 0.9072 |
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+ | 0.32 | 0.67 | 4000 | 0.3855 | 0.9074 |
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+ | 0.3655 | 0.75 | 4500 | 0.3848 | 0.9076 |
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+ | 0.3426 | 0.83 | 5000 | 0.3931 | 0.9072 |
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+ | 0.356 | 0.91 | 5500 | 0.3797 | 0.9089 |
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+ | 0.3965 | 1.0 | 6000 | 0.3767 | 0.9097 |
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  ### Framework versions
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