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ntu_adl_span_selection_roberta

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bert
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  1. README.md +12 -10
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -15,7 +15,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6539
 
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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- - train_batch_size: 4
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  - eval_batch_size: 1
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  - seed: 42
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- - gradient_accumulation_steps: 32
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- - total_train_batch_size: 128
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 2
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:-----:|:----:|:---------------:|
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- | No log | 1.0 | 169 | 0.6782 |
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- | No log | 1.99 | 338 | 0.6539 |
 
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  ### Framework versions
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  - Transformers 4.34.1
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  - Pytorch 2.1.0+cu118
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- - Datasets 2.14.5
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  - Tokenizers 0.14.1
 
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  This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1714
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+ - Em Accuracy: 0.7763
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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+ - train_batch_size: 1
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  - eval_batch_size: 1
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  - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 2
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 3
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Em Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:-----------:|
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+ | 0.9879 | 1.0 | 10857 | 0.9306 | 0.7441 |
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+ | 0.5075 | 2.0 | 21714 | 0.9758 | 0.7723 |
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+ | 0.2514 | 3.0 | 32571 | 1.1714 | 0.7763 |
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  ### Framework versions
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  - Transformers 4.34.1
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  - Pytorch 2.1.0+cu118
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+ - Datasets 2.14.6
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  - Tokenizers 0.14.1
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