From d8d5bf3b06389a65294ba13ae98efeda5b023c0d Mon Sep 17 00:00:00 2001 From: nlpzhezhao Date: Thu, 15 Feb 2024 02:16:47 +0800 Subject: [PATCH] update readme --- README.md | 7 +++---- README_ZH.md | 7 +++---- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index ca4bcae9..1ced3d87 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,6 @@ UER-py has the following features: * argparse * packaging * regex -* For the mixed precision training you will need apex from NVIDIA * For the pre-trained model conversion (related with TensorFlow) you will need TensorFlow * For the tokenization with sentencepiece model you will need [SentencePiece](https://github.com/google/sentencepiece) * For developing a stacking model you will need LightGBM and [BayesianOptimization](https://github.com/fmfn/BayesianOptimization) @@ -111,7 +110,7 @@ mv models/book_review_model.bin-5000 models/book_review_model.bin ``` Notice that the model trained by *pretrain.py* is attacted with the suffix which records the training step (*--total_steps*). We could remove the suffix for ease of use. -Then we fine-tune the pre-trained model on downstream classification dataset. We use embedding and encoder layers of book_review_model.bin, which is the output of *pretrain.py*: +Then we fine-tune the pre-trained model on downstream classification dataset. We use embedding and encoder layers of *book_review_model.bin*, which is the output of *pretrain.py*: ``` python3 finetune/run_classifier.py --pretrained_model_path models/book_review_model.bin \ --vocab_path models/google_zh_vocab.txt \ @@ -142,7 +141,7 @@ The above content provides basic ways of using UER-py to pre-process, pre-train,
## Pre-training data -This section provides links to a range of :arrow_right: [__pre-training data__](https://github.com/dbiir/UER-py/wiki/Pretraining-data) :arrow_left: . +This section provides links to a range of :arrow_right: [__pre-training data__](https://github.com/dbiir/UER-py/wiki/Pretraining-data) :arrow_left: . UER can load these pre-training data directly.
@@ -152,7 +151,7 @@ This section provides links to a range of :arrow_right: [__downstream datasets__
## Modelzoo -With the help of UER, we pre-trained models of different properties (e.g. models based on different corpora, encoders, and targets). Detailed introduction of pre-trained models and their download links can be found in :arrow_right: [__modelzoo__](https://github.com/dbiir/UER-py/wiki/Modelzoo) :arrow_left: . All pre-trained models can be loaded by UER directly. More pre-trained models will be released in the future. +With the help of UER, we pre-trained models of different properties (e.g. models based on different corpora, encoders, and targets). Detailed introduction of pre-trained models and their download links can be found in :arrow_right: [__modelzoo__](https://github.com/dbiir/UER-py/wiki/Modelzoo) :arrow_left: . All pre-trained models can be loaded by UER directly.
diff --git a/README_ZH.md b/README_ZH.md index 24a76b53..d013b863 100644 --- a/README_ZH.md +++ b/README_ZH.md @@ -51,7 +51,6 @@ UER-py有如下几方面优势: * argparse * packaging * regex -* 如果使用混合精度，需要安装英伟达的apex * 如果涉及到TensorFlow模型的转换，需要安装TensorFlow * 如果在tokenizer中使用sentencepiece模型，需要安装[SentencePiece](https://github.com/google/sentencepiece) * 如果使用模型集成stacking，需要安装LightGBM和[BayesianOptimization](https://github.com/fmfn/BayesianOptimization) @@ -109,7 +108,7 @@ mv models/book_review_model.bin-5000 models/book_review_model.bin ``` 请注意，*pretrain.py*输出的模型会带有记录训练步数的后缀（*--total_steps*），这里我们可以删除后缀以方便使用。 -然后，我们在下游分类数据集上微调预训练模型，我们使用 *pretrain.py* 的输出book_review_model.bin（加载词向量层和编码层参数）： +然后，我们在下游分类数据集上微调预训练模型，我们使用 *pretrain.py* 的输出*book_review_model.bin*（加载词向量层和编码层参数）： ``` python3 finetune/run_classifier.py --pretrained_model_path models/book_review_model.bin \ --vocab_path models/google_zh_vocab.txt \ @@ -140,7 +139,7 @@ python3 inference/run_classifier_infer.py --load_model_path models/finetuned_mod
## 预训练数据 -我们提供了链接，指向一系列开源的 :arrow_right: [__预训练数据__](https://github.com/dbiir/UER-py/wiki/预训练数据) :arrow_left: 。 +我们提供了链接，指向一系列开源的 :arrow_right: [__预训练数据__](https://github.com/dbiir/UER-py/wiki/预训练数据) :arrow_left: 。UER可以直接加载这些预训练数据。
@@ -150,7 +149,7 @@ python3 inference/run_classifier_infer.py --load_model_path models/finetuned_mod
## 预训练模型仓库 -借助UER-py，我们训练不同性质的预训练模型（例如基于不同语料、编码器、目标任务）。用户可以在 :arrow_right: [__预训练模型仓库__](https://github.com/dbiir/UER-py/wiki/预训练模型仓库) :arrow_left: 中找到各种性质的预训练模型以及它们对应的描述和下载链接。所有预训练模型都可以由UER-py直接加载。将来我们会发布更多的预训练模型。 +借助UER-py，我们训练不同性质的预训练模型（例如基于不同语料、编码器、目标任务）。用户可以在 :arrow_right: [__预训练模型仓库__](https://github.com/dbiir/UER-py/wiki/预训练模型仓库) :arrow_left: 中找到各种性质的预训练模型以及它们对应的描述和下载链接。所有预训练模型都可以由UER-py直接加载。