我正试图在Google上实现XLNET。但我得到了以下问题。
ImportError:
XLNetTokenizer requires the SentencePiece library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/google/sentencepiece#installation and follow the ones
that match your environmen
我已经阅读了关于这个错误的所有其他问题,而且令人沮丧的是,没有人给出一个有效的解决方案。
如果我在cmd行中运行pip install sentencepiece,它将给出以下输出。
src/sentencepiece/sentencepiece_wrap.cxx(2809): fatal error C1083: Cannot open include file: 'sentencepiece_processor.h': No such file or directory
error: command 'C:\\Program Files (x86)\\
我有一个字符串和规则/映射来替换和不替换。
例如。
"This is an example sentence that needs to be processed into a new sentence."
"This is a second example sentence that shows how 'sentence' in 'sentencepiece' should not be replaced."
替换规则:
replace_dictionary = {'sentence': 'proces
我正在尝试探索
这是密码
!pip install transformers
from transformers import T5Tokenizer, T5ForConditionalGeneration
qa_input = """question: What is the capital of Syria? context: The name "Syria" historically referred to a wider region,
broadly synonymous with the Levant, and known in Arabi
我把这个问题建立在的基础上,但是的结构略有不同:
saved_model = loader_impl.parse_saved_model("/path_to/universal_sent_encoder")
graph = saved_model.meta_graphs[0].graph_def
fns = [f for f in graph.library.function if "ptb" in str(f).lower()][0].node_def
print(len(fns))
>>> 1272
nodes = [n for n i
我使用这个NLP文档进行了检查:
from inltk.inltk import tokenize
text="जो मुझको सताती है तुझे वो बातें आती है जब सामने तू होता नहीं बेचैनी बढ़ जाती है मैं रूठ "
tokenize(text ,'hi')
错误是:
RuntimeError: Internal: src/sentencepiece_processor.cc(890)
[model_proto->ParseFromArray(serialized.data(), s
我正在尝试从运行示例代码
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import sentencepiece
text_generator = hub.Module(
'https://tfhub.dev/google/bertseq2seq/roberta24_bbc/1')
input_documents = ['This is text from the first document.',
'This is text
我使用以下代码
!pip install datasets transformers[sentencepiece]
from transformers import pipeline
ner = pipeline("ner", grouped_entities=True, model='dbmdz/bert-large-cased-finetuned-conll03-english') #Named Entity Recognition (NER)
ner("My name is <Name> and I work at <Office
我正在学习变压器的预训练模型示例
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="joeddav/xlm-roberta-large-xnli")
我得到以下错误
ValueError: Couldn't instantiate the backend tokenizer from one of: (1) a `tokenizers` library serialization
我是C++的新手,我跟随为句子构建了一个custom_ops。他们解释说,在运行之后,我将得到一个构建文件。
但是,在运行脚本之后,我没有看到构建文件。我看见-- Build files have been written to: /my_path/serving/tensorflow_serving/custom_ops/sentencepiece_processor/build了。搜索后,我发现构建文件是用于Bazel的,CMake不创建构建文件。如果是这样的话,我如何从CMake获得一个构建文件?谢谢。
我想尝试一下tensorflow-hub (通用句子编码器)中提供的嵌入。我尝试了提供的示例(),它运行得很好。因此,我试图对“多语言”模型进行同样的处理,但是每次加载多语言模型时,colab内核都会失败并重新启动。有什么问题,我怎样才能解决这个问题?
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
import
我试图加载一个预先训练的BERT模型,使用变压器库在一个起重器培训工作中,我得到了“没有模块命名的keras错误”。您可以在下面找到相关的代码、导入和requirements.txt
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from tensorflow.keras import