bart4csc-base-chinese
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bert4ner-base-chinese
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View details ↗chinese-text-correction-1.5b
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View details ↗gpt2-dialogbot-base-chinese
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View details ↗macbert4csc-base-chinese
shibing624本项目开源在中文文本纠错项目：pycorrector，可支持macbert4csc模型，通过如下命令调用： 如果需要训练macbert4csc，请参考https://github.com/shibing624/pycorrector/tree/master/pycorrector/macbert **MacBERT** is an improved BERT with novel **M**LM **a**s **c**orrection pre-training task, which mitigates the discrepancy of pre-training and fine-tuning.
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View details ↗mengzi-t5-base-chinese-correction
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View details ↗parrots-chinese-roberta-wwm-ext-large
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View details ↗text2vec-base-chinese
shibing624It maps sentences to a 768 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. For an automated evaluation of this model, see the *Evaluation Benchmark*: text2vec 说明： - 结果评测指标：spearman系数 - `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行examples/training_sup_text_matching_model.py代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset训练得到，并在中文各NLI测试集评估达到较好效果，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset，数据集相对于shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用 - `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`模型是用SBERT训练，是`paraphrase-MiniLM-L12-v2`模型的多语言版本，支持中文、英文等 - `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况
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View details ↗text2vec-base-chinese-paraphrase
shibing624It maps sentences to a 768 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. 说明： - 结果评测指标：spearman系数 - `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行examples/training_sup_text_matching_model.py代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset训练得到，并在中文各NLI测试集评估达到较好效果，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset，数据集相对于shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用 - `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`模型是用SBERT训练，是`paraphrase-MiniLM-L12-v2`模型的多语言版本，支持中文、英文等 - `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况 First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗text2vec-base-multilingual
shibing624It maps sentences to a 384 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. For an automated evaluation of this model, see the *Evaluation Benchmark*: text2vec Available languages are: de, en, es, fr, it, nl, pl, pt, ru, zh 说明： - 结果评测指标：spearman系数 - `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行examples/training_sup_text_matching_model.py代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset训练得到，并在中文各NLI测试集评估达到较好效果，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset，数据集相对于shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用 - `shibing624/text2vec-base-multilingual`模型，是用CoSENT方法训练，基于`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`用人工挑选后的多语言STS数据集shibing624/nli-zh-all/text2vec-base-multilingual-dataset训练得到，并在中英文测试集评估相对于原模型效果有提升，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，多语言语义匹配任务推荐使用 - `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况 - QPS的GPU测试环境是Tesla V100，显存32GB
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