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@@ -78,8 +78,8 @@ Here we provided several pretrained models on different datasets. The details of
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### Punctuation Restoration
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-| Model Name | Language ｜ Training Data | Parameters | Vocab Size| Offline/Online | Notes |
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-|:--------------------------------------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:----------:|:--------------:|:------|
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+| Model Name | Language | Training Data | Parameters | Vocab Size| Offline/Online | Notes |
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+|:--------------------------------------------------------------------------------------------------------------------------:|:---------|:----------------------------:|:----------:|:----------:|:--------------:|:------|
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| [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) | CN & EN | Alibaba Text Data | 100M | 471067 | Offline | large offline punctuation model |
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| [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary) | CN & EN | Alibaba Text Data | 70M | 272727 | Offline | offline punctuation model |
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| [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/summary) | CN & EN | Alibaba Text Data | 70M | 272727 | Online | online punctuation model |
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chenmengzheAAA