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@@ -2,43 +2,28 @@ |
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这里复现了在fastNLP中实现的模型,旨在达到与论文中相符的性能。 |
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复现的模型有: |
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- Star-Transformer |
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- [Star-Transformer](Star-transformer/) |
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- ... |
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# 任务复现 |
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## Text Classification (文本分类) |
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- still in progress |
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## Matching (自然语言推理/句子匹配) |
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- still in progress |
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## Sequence Labeling (序列标注) |
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- still in progress |
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## Coreference resolution (指代消解) |
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- still in progress |
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## Summarization (摘要) |
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- still in progress |
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## Star-Transformer |
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[reference](https://arxiv.org/abs/1902.09113) |
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### Performance (still in progress) |
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|任务| 数据集 | SOTA | 模型表现 | |
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|------|------| ------| ------| |
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|Pos Tagging|CTB 9.0|-|ACC 92.31| |
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|Pos Tagging|CONLL 2012|-|ACC 96.51| |
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|Named Entity Recognition|CONLL 2012|-|F1 85.66| |
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|Text Classification|SST|-|49.18| |
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|Natural Language Inference|SNLI|-|83.76| |
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### Usage |
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``` python |
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# for sequence labeling(ner, pos tagging, etc) |
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from fastNLP.models.star_transformer import STSeqLabel |
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model = STSeqLabel( |
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vocab_size=10000, num_cls=50, |
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emb_dim=300) |
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# for sequence classification |
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from fastNLP.models.star_transformer import STSeqCls |
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model = STSeqCls( |
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vocab_size=10000, num_cls=50, |
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emb_dim=300) |
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# for natural language inference |
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from fastNLP.models.star_transformer import STNLICls |
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model = STNLICls( |
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vocab_size=10000, num_cls=50, |
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emb_dim=300) |
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``` |
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## ... |