使用UER-py库在下游数据集进一步预训练+微调
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使用UER-py库在下游数据集进一步预训练+微调
环境
- RTX3090
- pytorch 1.8.1
- python 3.8
- cuda 11.1
流程
# 切换路径 cd /hy-tmp # 下载UER并安装其他依赖包 git clone https://hub.fastgit.org/dbiir/UER-py.git cd UER-py pip install -U six pip install transformers
转化huggingface的预训练权重为UER适配的权重
convert.py如下
# convert.py import torch import argparse import collections def convert_bert_transformer_encoder_from_huggingface_to_uer(input_model, output_model, layers_num): for i in range(layers_num): output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.0.weight"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.query.weight"] output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.0.bias"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.query.bias"] output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.1.weight"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.key.weight"] output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.1.bias"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.key.bias"] output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.2.weight"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.value.weight"] output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.2.bias"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.value.bias"] output_model["encoder.transformer." + str(i) + ".self_attn.final_linear.weight"] = input_model["bert.encoder.layer." + str(i) + ".attention.output.dense.weight"] output_model["encoder.transformer." + str(i) + ".self_attn.final_linear.bias"] = input_model["bert.encoder.layer." + str(i) + ".attention.output.dense.bias"] output_model["encoder.transformer." + str(i) + ".layer_norm_1.gamma"] = input_model["bert.encoder.layer." + str(i) + ".attention.output.LayerNorm.weight"] output_model["encoder.transformer." + str(i) + ".layer_norm_1.beta"] = input_model["bert.encoder.layer." + str(i) + ".attention.output.LayerNorm.bias"] output_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.weight"] = input_model["bert.encoder.layer." + str(i) + ".intermediate.dense.weight"] output_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.bias"] = input_model["bert.encoder.layer." + str(i) + ".intermediate.dense.bias"] output_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.weight"] = input_model["bert.encoder.layer." + str(i) + ".output.dense.weight"] output_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.bias"] = input_model["bert.encoder.layer." + str(i) + ".output.dense.bias"] output_model["encoder.transformer." + str(i) + ".layer_norm_2.gamma"] = input_model["bert.encoder.layer." + str(i) + ".output.LayerNorm.weight"] output_model["encoder.transformer." + str(i) + ".layer_norm_2.beta"] = input_model["bert.encoder.layer." + str(i) + ".output.LayerNorm.bias"] def main(): parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("--input_model_path", type=str, default="models/bert-base-chinese/pytorch_model.bin", help=".") parser.add_argument("--output_model_path", type=str, default="models/google_zh_model.bin", help=".") parser.add_argument("--layers_num", type=int, default=12, help=".") parser.add_argument("--target", choices=["bert", "mlm"], default="bert", help="The training target of the pretraining model.") args = parser.parse_args() input_model = torch.load(args.input_model_path, map_location='cpu') output_model = collections.OrderedDict() output_model["embedding.word_embedding.weight"] = input_model["bert.embeddings.word_embeddings.weight"] output_model["embedding.position_embedding.weight"] = input_model["bert.embeddings.position_embeddings.weight"] output_model["embedding.segment_embedding.weight"] = torch.cat((torch.Tensor([[0]*input_model["bert.embeddings.token_type_embeddings.weight"].size()[1]]), input_model["bert.embeddings.token_type_embeddings.weight"]), dim=0) output_model["embedding.layer_norm.gamma"] = input_model["bert.embeddings.LayerNorm.weight"] output_model["embedding.layer_norm.beta"] = input_model["bert.embeddings.LayerNorm.bias"] convert_bert_transformer_encoder_from_huggingface_to_uer(input_model, output_model, args.layers_num) if args.target == "bert": output_model["target.nsp_linear_1.weight"] = input_model["bert.pooler.dense.weight"] output_model["target.nsp_linear_1.bias"] = input_model["bert.pooler.dense.bias"] output_model["target.nsp_linear_2.weight"] = input_model["cls.seq_relationship.weight"] output_model["target.nsp_linear_2.bias"] = input_model["cls.seq_relationship.bias"] output_model["target.mlm_linear_1.weight"] = input_model["cls.predictions.transform.dense.weight"] output_model["target.mlm_linear_1.bias"] = input_model["cls.predictions.transform.dense.bias"] output_model["target.layer_norm.gamma"] = input_model["cls.predictions.transform.LayerNorm.weight"] output_model["target.layer_norm.beta"] = input_model["cls.predictions.transform.LayerNorm.bias"] output_model["target.mlm_linear_2.weight"] = input_model["cls.predictions.decoder.weight"] output_model["target.mlm_linear_2.bias"] = input_model["cls.predictions.bias"] torch.save(output_model, args.output_model_path) if __name__ == "__main__": from transformers import BertForPreTraining model = BertForPreTraining.from_pretrained("bert-base-chinese") model.save_pretrained("models/bert-base-chinese") main()
# 创建convert.py并将代码复制进去 touch convert.py # 转换权重 python convert.py
# 数据预处理 python preprocess.py --corpus_path corpora/book_review_bert.txt \ --vocab_path models/google_zh_vocab.txt \ --dataset_path dataset.pt --processes_num 8 \ --target bert
# 进一步预训练 python pretrain.py --dataset_path dataset.pt --vocab_path models/google_zh_vocab.txt \ --pretrained_model_path models/google_zh_model.bin \ --output_model_path models/book_review_model.bin \ --gpu_ranks 0 \ --total_steps 5000 --save_checkpoint_steps 1000 --batch_size 16 \ --embedding word_pos_seg --encoder transformer --mask fully_visible --target bert # 修改权重名 mv models/book_review_model.bin-5000 models/book_review_model.bin
# 微调 python finetune/run_classifier.py --pretrained_model_path models/book_review_model.bin \ --vocab_path models/google_zh_vocab.txt \ --train_path datasets/douban_book_review/train.tsv \ --dev_path datasets/douban_book_review/dev.tsv \ --test_path datasets/douban_book_review/test.tsv \ --epochs_num 3 --batch_size 64 \ --embedding word_pos_seg --encoder transformer --mask fully_visible
REFERENCE
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UER-py(Universal Encoder Representations)是一个用于对通用语料进行预训练并对下游任务进行微调的工具包
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Alice_恒源云
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