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README.md
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## Model List
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The evaluation dataset is in Chinese, and we used the same language model **RoBERTa base** on different methods.
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| Model | STS-B(w-avg) | ATEC | BQ | LCQMC | PAWSX | Avg. |
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| BERT-Whitening | 65.27| -| -| -| -| -|
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| SimBERT | 70.01| -| -| -| -| -|
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| SBERT-Whitening | 71.75| -| -| -| -| -|
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| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 78.61| -| -| -| -| -|
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| [hellonlp/simcse-base-zh](https://huggingface.co/hellonlp/simcse-roberta-base-zh) | 80.96| -| -| -| -| -|
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| [hellonlp/promcse-base-zh](https://huggingface.co/hellonlp/promcse-bert-base-zh) | **81.57**| -| -| -| -| -|
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## Data List
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The following datasets are all in Chinese.
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## Uses
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To use the tool, first install the `promcse` package from [PyPI](https://pypi.org/project/promcse/)
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```bash
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## Data List
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The following datasets are all in Chinese.
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## Model List
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The evaluation dataset is in Chinese, and we used the same language model **RoBERTa base** on different methods.
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Considering that the test set of some data sets is small, which may lead to a large deviation in evaluation accuracy, the evaluation data here uses train, valid and test at the same time, and the final evaluation result adopts the weighted average (w-avg) method. .
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| Model | STS-B(w-avg) | ATEC | BQ | LCQMC | PAWSX | Avg. |
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|:-----------------------:|:------------:|:-----------:|:----------|:-------------|:------------:|:----------:|
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| BERT-Whitening | 65.27| -| -| -| -| -|
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| SimBERT | 70.01| -| -| -| -| -|
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| SBERT-Whitening | 71.75| -| -| -| -| -|
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| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 78.61| -| -| -| -| -|
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| [hellonlp/simcse-base-zh](https://huggingface.co/hellonlp/simcse-roberta-base-zh) | 80.96| -| -| -| -| -|
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| [hellonlp/promcse-base-zh](https://huggingface.co/hellonlp/promcse-bert-base-zh) | **81.57**| -| -| -| -| -|
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## Uses
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To use the tool, first install the `promcse` package from [PyPI](https://pypi.org/project/promcse/)
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```bash
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