机器学习-bert模型

# -*- coding: utf-8 -*- """ Created on Mon Jan 25 21:58:51 2021 @author: admin """ #设置和导入 import seaborn as sns from sklearn.metrics import pairwise import tensorflow as tf import tensorflow_hub as hub import tensorflow_text as text # Imports TF ops for preprocessing. #配置模型 BERT_MODEL = "https://hub.tensorflow.google.cn/google/experts/bert/wiki_books/2" # @param {type: "string"} ["https://hub.tensorflow.google.cn/google/experts/bert/wiki_books/2", "https://hub.tensorflow.google.cn/google/experts/bert/wiki_books/mnli/2", "https://hub.tensorflow.google.cn/google/experts/bert/wiki_books/qnli/2", "https://hub.tensorflow.google.cn/google/experts/bert/wiki_books/qqp/2", "https://hub.tensorflow.google.cn/google/experts/bert/wiki_books/squad2/2", "https://hub.tensorflow.google.cn/google/experts/bert/wiki_books/sst2/2", "https://hub.tensorflow.google.cn/google/experts/bert/pubmed/2", "https://tfhub.dev/google/experts/bert/pubmed/squad2/2"] # Preprocessing must match the model, but all the above use the same. PREPROCESS_MODEL = "https://hub.tensorflow.google.cn/tensorflow/bert_en_uncased_preprocess/1" #句子 sentences = [ "Here We Go Then, You And I is a 1999 album by Norwegian pop artist Morten Abel. It was Abel's second CD as a solo artist.", "The album went straight to number one on the Norwegian album chart, and sold to double platinum.", "Among the singles released from the album were the songs \"Be My Lover\" and \"Hard To Stay Awake\".", "Riccardo Zegna is an Italian jazz musician.", "Rajko Maksimović is a composer, writer, and music pedagogue.", "One of the most significant Serbian composers of our time, Maksimović has been and remains active in creating works for different ensembles.", "Ceylon spinach is a common name for several plants and may refer to: Basella alba Talinum fruticosum", "A solar eclipse occurs when the Moon passes between Earth and the Sun, thereby totally or partly obscuring the image of the Sun for a viewer on Earth.", "A partial solar eclipse occurs in the polar regions of the Earth when the center of the Moon's shadow misses the Earth.", ] #运行模型 preprocess = hub.load(PREPROCESS_MODEL) bert = hub.load(BERT_MODEL) inputs = preprocess(sentences) outputs = bert(inputs) #print("Sentences:") print(sentences) print("\nBERT inputs:") print(inputs) print("\nPooled embeddings:") print(outputs["pooled_output"]) print("\nPer token embeddings:") print(outputs["sequence_output"]) #语义相似度 def plot_similarity(features, labels): """Plot a similarity matrix of the embeddings.""" cos_sim = pairwise.cosine_similarity(features) sns.set(font_scale=1.2) cbar_kws=dict(use_gridspec=False, location="left") g = sns.heatmap( cos_sim, xticklabels=labels, yticklabels=labels, vmin=0, vmax=1, cmap="Blues", cbar_kws=cbar_kws) g.tick_params(labelright=True, labelleft=False) g.set_yticklabels(labels, rotation=0) g.set_title("Semantic Textual Similarity") plot_similarity(outputs["pooled_output"], sentences) import matplotlib.pyplot as plt plt.show()