import tensorflow as tf #Tokenizer 的示例 tokenizer = tf.keras.preprocessing.text.Tokenizer( filters='') text = ["昨天 天气 是 多云", "我 今天 做了 什么 呢"] tokenizer.fit_on_texts(text) tensorr = tokenizer.texts_to_sequences(text) print(tensorr) 1. 2. 3. 4. 5. 6. 7. 8. ...
from sklearn.preprocessing import OneHotEncoder 1. 当然keras也有 keras.preprocessing.text.one_hot(text, n, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=True, split=' ') 1. tf_text 但是我并不是写这么东西tensorflow-text,主要是想记录下tensorflow-text,这是tf2.0新东西...
keras.preprocessing.text.one_hot(text,n,filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',lower=True,split=' ') tf_text 但是我并不是写这么东西tensorflow-text,主要是想记录下tensorflow-text,这是tf2.0新东西,1.0多没有见过,所以记录下 注意: 需要TensorFlow 2.0 安装 代码语言:javascrip...
from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences import re #--- # Clean the captions data # Convert all words to lowercase. # Remove all punctuation. # Remove all words that are one character or less in length (e.g. ‘a’). # Remove ...
from flask import Flask from flask import request from keras.preprocessing import sequence from keras.preprocessing.text import Tokenizer from keras.models import load_model import json app = Flask(__name__) #加载词对应的数字枚举 & 加载模型 model = load_model('D:\\mini_test_model.h5') token...
问用HuggingFace的变压器用TFBertModel和AutoTokenizer建立模型时的输入问题EN在之前对 ChatGLM 的搭建部署...
用tf.Tokenizer我得到了文本A的word_index a_tokenizer = Tokenizer() a_tokenizer.fit_on_texts(textA) #Builds the word index word_index=a_tokenizer.word_index 文本B的word_count b_tokenizer = Tokenizer() b_tokenizer.fit_on_texts(generated_text) ...
ValueError: ('`tf.compat.v1.keras` Optimizer (', <tensorflow.python.keras.optimizers.SGD >, ') is not supported when eager execution is enabled. Use a `tf.keras` Optimizer instead, or disable eager execution.') ValueError: ('`tf.compat.v1.keras` Optimizer (', <tensorflow.python.keras...
RuntimeError: module compiled against API version 0xa but this version of numpy is 0x9 Traceback (most recent call last): File "/almac/ignacio/nlp-pipeline/recurrent_ltsm.py", line 126, in <module> from keras.preprocessing.text import Tokenizer File "/usr/local/lib/python2.7/dist-packages...
To create a SavedModel, the Transformers library lets you load a PyTorch model called nateraw/bert-base-uncased-imdb trained on the IMBD dataset and convert it to a TensorFlow Keras model for you: from transformers import TFBertForSequenceClassification model = TFBertForSequenceClassification.from...