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@@ -9,7 +9,8 @@ In this paper, we propose DictBERT, which is a novel pre-trained language model
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  ## Evaluation results
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- When fine-tuned BERT and our DictBERT on GLEU benchmarks tasks. CoLA is evaluated by matthews, STS-B is evaluated by pearson, and others are evaluated by accuracy. DictBERT model achieves the following results:
 
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  | | MNLI | QNLI | QQP | SST-2 | CoLA | MRPC | RTE | STS-B | Average |
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  |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
@@ -18,6 +19,13 @@ When fine-tuned BERT and our DictBERT on GLEU benchmarks tasks. CoLA is evaluate
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  HF: huggingface checkpoint for BERT-base uncased
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  ### BibTeX entry and citation info
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  ```bibtex
 
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  ## Evaluation results
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+ We show performance of fine-tuning BERT and DictBERT on the GLEU benchmarks tasks. CoLA is evaluated by matthews, STS-B is evaluated by pearson, and other tasks are evaluated by accuracy. The models achieve the following results:
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  | | MNLI | QNLI | QQP | SST-2 | CoLA | MRPC | RTE | STS-B | Average |
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  |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
 
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  HF: huggingface checkpoint for BERT-base uncased
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+ If no dictionary if provided during fine-tuning, DictBERT can still achieve better performance than BERT.
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+ | | MNLI | QNLI | QQP | SST-2 | CoLA | MRPC | RTE | STS-B | Average |
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+ |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
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+ | w/o dict | 84.24 | 90.99 | 90.80 | 92.51 | 60.50 | 87.04 | 73.75 | 89.37 | 83.69 |
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  ### BibTeX entry and citation info
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  ```bibtex