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您好，我一直在尝试使用新颖的 XLNet 上下文提取词嵌入，但没有运气。

使用 TPU 在 Google Colab 上运行

我想指出，当我使用 TPU 时出现此错误，因此我切换到 GPU 以避免该错误

xlnet_config = xlnet.XLNetConfig(json_path=FLAGS.model_config_path)

AttributeError：模块“xlnet”没有属性“XLNetConfig”

但是，当我使用 GPU 时出现另一个错误

run_config = xlnet.create_run_config(is_training=True, is_finetune=True, FLAGS=FLAGS)

属性错误：use_tpu

我将在下面发布整个代码：我使用一个小句子作为输入，直到它起作用，然后我切换到大数据

主要代码：

import sentencepiece as spm
import numpy as np
import tensorflow as tf
from prepro_utils import preprocess_text, encode_ids
import xlnet
import sentencepiece as spm

text = "The metamorphic rocks of western Crete form a series some 9000 to 10,000 ft."
sp_model = spm.SentencePieceProcessor()
sp_model.Load("/content/xlnet_cased_L-24_H-1024_A-16/spiece.model")

text = preprocess_text(text) 
ids = encode_ids(sp_model, text)

#print('ids',ids)

# some code omitted here...
# initialize FLAGS
# initialize instances of tf.Tensor, including input_ids, seg_ids, and input_mask

# XLNetConfig contains hyperparameters that are specific to a model checkpoint.
xlnet_config = xlnet.XLNetConfig(json_path=FLAGS.model_config_path) **ERROR 1 HERE**
from absl import flags
import sys

FLAGS = flags.FLAGS
# RunConfig contains hyperparameters that could be different between pretraining and finetuning.
run_config = xlnet.create_run_config(is_training=True, is_finetune=True, FLAGS=FLAGS) **ERROR 2 HERE**
xp = []
xp.append(ids)
input_ids = np.asarray(xp)
xlnet_model = xlnet.XLNetModel(
    xlnet_config=xlnet_config,
    run_config=run_config,
    input_ids=input_ids,
    seg_ids=None,
    input_mask=None)
embed1=tf.train.load_variable('../data/xlnet_cased_L-24_H-1024_A-16/xlnet_model.ckpt','model/transformer/word_embedding/lookup_table:0')`

在主要代码之前我正在从 GitHub 克隆 Xlnet 等等（我也会发布它）

! pip install sentencepiece
#Download the pretrained XLNet model and unzip only needs to be done once
! wget https://storage.googleapis.com/xlnet/released_models/cased_L-24_H-1024_A-16.zip
! unzip cased_L-24_H-1024_A-16.zip
! git clone https://github.com/zihangdai/xlnet.git

SCRIPTS_DIR = 'xlnet' #@param {type:"string"}
DATA_DIR = 'aclImdb' #@param {type:"string"}
OUTPUT_DIR = 'proc_data/imdb' #@param {type:"string"}
PRETRAINED_MODEL_DIR = 'xlnet_cased_L-24_H-1024_A-16' #@param {type:"string"}
CHECKPOINT_DIR = 'exp/imdb' #@param {type:"string"}

train_command = "python xlnet/run_classifier.py \
  --do_train=True \
  --do_eval=True \
  --eval_all_ckpt=True \
  --task_name=imdb \
  --data_dir="+DATA_DIR+" \
  --output_dir="+OUTPUT_DIR+" \
  --model_dir="+CHECKPOINT_DIR+" \
  --uncased=False \
  --spiece_model_file="+PRETRAINED_MODEL_DIR+"/spiece.model \
  --model_config_path="+PRETRAINED_MODEL_DIR+"/xlnet_config.json \
  --init_checkpoint="+PRETRAINED_MODEL_DIR+"/xlnet_model.ckpt \
  --max_seq_length=128 \
  --train_batch_size=8 \
  --eval_batch_size=8 \
  --num_hosts=1 \
  --num_core_per_host=1 \
  --learning_rate=2e-5 \
  --train_steps=4000 \
  --warmup_steps=500 \
  --save_steps=500 \
  --iterations=500"

! {train_command}
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1 回答 1

2

检查这个要点。

我们已经很容易从XLNet

更新：更新要点。

有关详细文档和更多示例，请查看Github

于 2019-08-07T11:07:32.843 回答