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A Large-scale Chinese Short-Text Conversation Dataset and Chinese pre-training dialog models

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ERROR:ignite.engine.engine.Engine:Current run is terminating due to exception: unsupported operand type(s) for /: 'str' and 'int'. /opt/conda/conda-bld/pytorch_1634272168290/work/aten/src/ATen/native/cuda/Loss.cu:247: nll_loss_forward_reduce_cuda_kernel_2d: block: [0,0,0], thread: [0,0,0] Assertion `t >= 0 && t < n_classes` failed....

请问已经生成STC_result.txt,想生成像Readme 评价指标表格中的 PPL | BLEU-2 | BLEU-4 | Dist-1 | Dist-2 | Greedy Matching | Embedding Average 这些值,请问你们是怎么计算的?

你还根据https://github.com/thu-coai/CDial-GPT/issues/53 和https://github.com/thu-coai/CDial-GPT/issues/55 两个issue的指导复现embedding average的计算,发现ground truth中有英文语句,中文分词方法不太适用英文的分词,请问你们是怎么处理的呢?直接丢弃还是适用英文分词方法对英文的ground truth进行分词。例如在STC_test.json中存在“"I f o n l y w e c o u l d s e e t h e w o r l d i...

AILab的向量都是中文词向量,而在STC数据集生成的都是以字为单位,请问您在测试的时候,是将STC_test和模型生成的文本转换成词的形式计算,还是没经过分词处理,直接计算Embedding Average和Greedy Matching

请问对话语每个字的字嵌入向量编码的编码是哪个变量?在代码中没找到word_embedding变量,请问有字嵌入向量编码的变量吗?

您好,我按照您提供的GREEDY MATCHINHG 和EMBEDDING AVERAGE的代码进行评价,发现速度非常慢,想问一下您当时是用什么配置进行评价操作的?

出现报错:C:/cb/pytorch_1000000000000/work/aten/src/THCUNN/ClassNLLCriterion.cu:108: block: [0,0,0], thread: [0,0,0] Assertion `t >= 0 && t < n_classes` failed. C:/cb/pytorch_1000000000000/work/aten/src/THCUNN/ClassNLLCriterion.cu:108: block: [0,0,0], thread: [1,0,0] Assertion `t >= 0 && t < n_classes` failed. C:/cb/pytorch_1000000000000/work/aten/src/THCUNN/ClassNLLCriterion.cu:108: block:...

Bumps [transformers](https://github.com/huggingface/transformers) from 2.1.1 to 4.30.0. Release notes Sourced from transformers's releases. v4.30.0: 100k, Agents improvements, Safetensors core dependency, Swiftformer, Autoformer, MobileViTv2, timm-as-a-backbone 100k Transformers has just reached 100k stars...

dependencies