Imposingapple
Imposingapple
Thank you for the amazing job! I've run the experiment of LPIPS distance metric and found that my LPIPS distance is larger than the one in your paper. Specific speaking:...
作者您好, 我看了您的说明《更多预训练模型》,然后想用您已经给的‘book_review.txt‘数据跑一个非常小的GPT-2的微调 我依次运行代码(有4块机子,故world_size我设为4,用两块空余的GPU跑) python3 preprocess.py --corpus_path corpora/book_review.txt --vocab_path models/google_zh_vocab.txt \ --dataset_path dataset.pt --processes_num 8 --target lm python3 pretrain.py --dataset_path dataset.pt --vocab_path models/google_zh_vocab.txt --output_model_path models/output_model.bin \ --config_path models/bert_base_config.json --learning_rate 1e-4 \...
您好,我使用UER的GPT-2预训练方法训练了一个古诗的模型,然后做预测的时候发现生成的就好像是随机的文本,有时甚至还有很多[UNK],想请教下这是为什么?  我的输入是“床前明月光,” ------------------------------------------------------------------------------------------------------------------ 我的数据是nlp_chinese_corpus这个仓库中的中国诗词,我按照book_review.txt的形式,一行放了一首诗,然后按照您给的GPT-2预训练示例的输入指令进行预处理与预训练,我的数据文本大概是这样子的  预训练的指令如下: CUDA_VISIBLE_DEVICES=1 python3 pretrain.py --dataset_path datasets/poems.pt --vocab_path models/google_zh_vocab.txt --output_model_path models/poems_model.bin --config_path models/gpt2/config.json --learning_rate 1e-4 --world_size 1 --gpu_ranks 0 --tie_weight --embedding word_pos --remove_embedding_layernorm --encoder transformer...
After doing the data preprocessing, training and evaluation for humorous headline generation provided by this repo, i get the following result. The BLEU score is 9.0, much lower than you...