Comments (2)
T5是encoder-decoder架构的模型,属于条件生成(condition-generate),如果使用因果语言模型(causal LM)类似格式进行无监督预训练,要大改训练代码。
使用<prompt,response >格式进行预训练算是一种尝试吧,效果这块,我没有做过消融实验,无法下结论。
如果你需要decoder-only无监督预训练模型,请参考这个项目Phi2-mini-Chinese
整理text-to-text预训练数据的原因:受到微软phi系列模型的启发,phi系列模型使用高质量数据集进行预训练,效果很好,text-to-text高质量数据是最好获取的。
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感谢回复!
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Related Issues (20)
- 多卡情况下,同一份数据集会加载多次吗 HOT 6
- 请问,如果有新的内容需要添加,是否需要全部重新训练? HOT 2
- 请问数据预处理里面bell_open_source/train_0.8M_CN.json是在哪里下载的呀 HOT 7
- 运行·pre_train报错,TypeError: Accelerator.__init__() got an unexpected keyword argument 'use_seedable_sampler' HOT 1
- Great Work! Does it support multimodal ability? HOT 1
- 项目怎么使用fastchat 进行调试 HOT 1
- 微调后预测三元组不正确原因 HOT 5
- 用train.py出现shape的mismatch HOT 10
- sft微调时报错 HOT 4
- 如何提取中间层的输出? HOT 2
- 考虑出一个支持llama的版本吗 HOT 1
- RuntimeError: No executable batch size found, reached zero HOT 2
- 如何加载sft后的模型? HOT 1
- train_3.5M_CN数据处理问题 HOT 1
- 这个模型好像没有长文对话的能力,该如何训练它让它有这个能力? HOT 1
- 请问这些预训练数据加起来有多少token呀 HOT 2
- 非常不错的开源项目 HOT 1
- 预训练数据集必须是{“prompt”: "response":}的格式么? HOT 2
- Some NCCL operations have failed or timed out. HOT 5
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