探索 TF-Hub CORD-19 枢纽嵌入

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查看 TF Hub 模型

来自 TF-Hub 的 CORD-19 枢纽文本嵌入模块 (https://tfhub.dev/tensorflow/cord-19/swivel-128d/3) 是为了支持研究人员分析与 COVID-19 相关的自然语言文本而构建的。这些嵌入是在 CORD-19 数据集中的文章标题、作者、摘要、正文和参考文献标题上训练的。

在这个 colab 中，我们将

分析嵌入空间中语义相似的词
使用 CORD-19 嵌入在 SciCite 数据集上训练分类器

设置

import functools
import itertools
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd

import tensorflow as tf

import tensorflow_datasets as tfds
import tensorflow_hub as hub

from tqdm import trange

2023-10-09 22:29:39.063949: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-10-09 22:29:39.064004: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-10-09 22:29:39.064046: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

分析嵌入

让我们从通过计算和绘制不同术语之间的相关矩阵来分析嵌入开始。如果嵌入成功地学习了如何捕捉不同词的含义，那么语义相似的词的嵌入向量应该彼此靠近。让我们看看一些与 COVID-19 相关的术语。

# Use the inner product between two embedding vectors as the similarity measure
def plot_correlation(labels, features):
  corr = np.inner(features, features)
  corr /= np.max(corr)
  sns.heatmap(corr, xticklabels=labels, yticklabels=labels)

# Generate embeddings for some terms
queries = [
  # Related viruses
  'coronavirus', 'SARS', 'MERS',
  # Regions
  'Italy', 'Spain', 'Europe',
  # Symptoms
  'cough', 'fever', 'throat'
]

module = hub.load('https://tfhub.dev/tensorflow/cord-19/swivel-128d/3')
embeddings = module(queries)

plot_correlation(queries, embeddings)

2023-10-09 22:29:44.568345: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

png

我们可以看到，嵌入成功地捕捉了不同术语的含义。每个词都与其集群中的其他词相似（例如，“冠状病毒”与“SARS”和“MERS”高度相关），而它们与其他集群的术语不同（例如，“SARS”和“西班牙”之间的相似度接近 0）。

现在让我们看看如何使用这些嵌入来解决特定任务。

SciCite：引用意图分类

本节展示了如何将嵌入用于下游任务，例如文本分类。我们将使用 TensorFlow 数据集中的 SciCite 数据集来对学术论文中的引用意图进行分类。给定包含来自学术论文的引用的句子，对引用的主要意图进行分类，例如背景信息、方法使用或结果比较。

builder = tfds.builder(name='scicite')
builder.download_and_prepare()
train_data, validation_data, test_data = builder.as_dataset(
    split=('train', 'validation', 'test'),
    as_supervised=True)

让我们看看训练集中的一些带标签的示例

NUM_EXAMPLES =   10

TEXT_FEATURE_NAME = builder.info.supervised_keys[0]
LABEL_NAME = builder.info.supervised_keys[1]

def label2str(numeric_label):
  m = builder.info.features[LABEL_NAME].names
  return m[numeric_label]

data = next(iter(train_data.batch(NUM_EXAMPLES)))


pd.DataFrame({
    TEXT_FEATURE_NAME: [ex.numpy().decode('utf8') for ex in data[0]],
    LABEL_NAME: [label2str(x) for x in data[1]]
})

训练引用意图分类器

我们将使用 Keras 在 SciCite 数据集上训练分类器。让我们构建一个模型，该模型使用 CORD-19 嵌入，并在其顶部使用一个分类层。

超参数

EMBEDDING = 'https://tfhub.dev/tensorflow/cord-19/swivel-128d/3' 
TRAINABLE_MODULE = False 

hub_layer = hub.KerasLayer(EMBEDDING, input_shape=[], 
                           dtype=tf.string, trainable=TRAINABLE_MODULE)

model = tf.keras.Sequential()
model.add(hub_layer)
model.add(tf.keras.layers.Dense(3))
model.summary()
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 keras_layer (KerasLayer)    (None, 128)               17301632  
                                                                 
 dense (Dense)               (None, 3)                 387       
                                                                 
=================================================================
Total params: 17302019 (132.00 MB)
Trainable params: 387 (1.51 KB)
Non-trainable params: 17301632 (132.00 MB)
_________________________________________________________________

训练和评估模型

让我们训练和评估模型，看看它在 SciCite 任务上的表现

EPOCHS = 35
BATCH_SIZE = 32

history = model.fit(train_data.shuffle(10000).batch(BATCH_SIZE),
                    epochs=EPOCHS,
                    validation_data=validation_data.batch(BATCH_SIZE),
                    verbose=1)

Epoch 1/35
257/257 [==============================] - 2s 4ms/step - loss: 0.8391 - accuracy: 0.6421 - val_loss: 0.7495 - val_accuracy: 0.7020
Epoch 2/35
257/257 [==============================] - 1s 3ms/step - loss: 0.6784 - accuracy: 0.7282 - val_loss: 0.6634 - val_accuracy: 0.7380
Epoch 3/35
257/257 [==============================] - 1s 3ms/step - loss: 0.6175 - accuracy: 0.7562 - val_loss: 0.6269 - val_accuracy: 0.7478
Epoch 4/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5858 - accuracy: 0.7706 - val_loss: 0.6035 - val_accuracy: 0.7533
Epoch 5/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5674 - accuracy: 0.7780 - val_loss: 0.5914 - val_accuracy: 0.7576
Epoch 6/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5553 - accuracy: 0.7817 - val_loss: 0.5822 - val_accuracy: 0.7653
Epoch 7/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5464 - accuracy: 0.7847 - val_loss: 0.5784 - val_accuracy: 0.7609
Epoch 8/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5399 - accuracy: 0.7872 - val_loss: 0.5723 - val_accuracy: 0.7707
Epoch 9/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5352 - accuracy: 0.7906 - val_loss: 0.5690 - val_accuracy: 0.7707
Epoch 10/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5303 - accuracy: 0.7924 - val_loss: 0.5630 - val_accuracy: 0.7806
Epoch 11/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5268 - accuracy: 0.7939 - val_loss: 0.5610 - val_accuracy: 0.7773
Epoch 12/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5236 - accuracy: 0.7929 - val_loss: 0.5601 - val_accuracy: 0.7762
Epoch 13/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5213 - accuracy: 0.7952 - val_loss: 0.5586 - val_accuracy: 0.7773
Epoch 14/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5188 - accuracy: 0.7959 - val_loss: 0.5560 - val_accuracy: 0.7751
Epoch 15/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5169 - accuracy: 0.7963 - val_loss: 0.5566 - val_accuracy: 0.7817
Epoch 16/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5150 - accuracy: 0.7950 - val_loss: 0.5521 - val_accuracy: 0.7795
Epoch 17/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5136 - accuracy: 0.7974 - val_loss: 0.5551 - val_accuracy: 0.7795
Epoch 18/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5122 - accuracy: 0.7966 - val_loss: 0.5490 - val_accuracy: 0.7795
Epoch 19/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5106 - accuracy: 0.7973 - val_loss: 0.5508 - val_accuracy: 0.7849
Epoch 20/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5097 - accuracy: 0.7974 - val_loss: 0.5503 - val_accuracy: 0.7806
Epoch 21/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5086 - accuracy: 0.7981 - val_loss: 0.5467 - val_accuracy: 0.7817
Epoch 22/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5072 - accuracy: 0.8003 - val_loss: 0.5518 - val_accuracy: 0.7838
Epoch 23/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5066 - accuracy: 0.7994 - val_loss: 0.5485 - val_accuracy: 0.7871
Epoch 24/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5060 - accuracy: 0.7991 - val_loss: 0.5477 - val_accuracy: 0.7849
Epoch 25/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5049 - accuracy: 0.8003 - val_loss: 0.5481 - val_accuracy: 0.7849
Epoch 26/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5046 - accuracy: 0.7985 - val_loss: 0.5465 - val_accuracy: 0.7871
Epoch 27/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5035 - accuracy: 0.7999 - val_loss: 0.5457 - val_accuracy: 0.7828
Epoch 28/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5030 - accuracy: 0.8011 - val_loss: 0.5474 - val_accuracy: 0.7838
Epoch 29/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5025 - accuracy: 0.8007 - val_loss: 0.5484 - val_accuracy: 0.7871
Epoch 30/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5016 - accuracy: 0.8023 - val_loss: 0.5440 - val_accuracy: 0.7904
Epoch 31/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5011 - accuracy: 0.8003 - val_loss: 0.5487 - val_accuracy: 0.7849
Epoch 32/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5011 - accuracy: 0.8012 - val_loss: 0.5451 - val_accuracy: 0.7882
Epoch 33/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5005 - accuracy: 0.8011 - val_loss: 0.5464 - val_accuracy: 0.7882
Epoch 34/35
257/257 [==============================] - 1s 3ms/step - loss: 0.5000 - accuracy: 0.8014 - val_loss: 0.5486 - val_accuracy: 0.7871
Epoch 35/35
257/257 [==============================] - 1s 3ms/step - loss: 0.4995 - accuracy: 0.8006 - val_loss: 0.5485 - val_accuracy: 0.7871

from matplotlib import pyplot as plt
def display_training_curves(training, validation, title, subplot):
  if subplot%10==1: # set up the subplots on the first call
    plt.subplots(figsize=(10,10), facecolor='#F0F0F0')
    plt.tight_layout()
  ax = plt.subplot(subplot)
  ax.set_facecolor('#F8F8F8')
  ax.plot(training)
  ax.plot(validation)
  ax.set_title('model '+ title)
  ax.set_ylabel(title)
  ax.set_xlabel('epoch')
  ax.legend(['train', 'valid.'])

display_training_curves(history.history['accuracy'], history.history['val_accuracy'], 'accuracy', 211)
display_training_curves(history.history['loss'], history.history['val_loss'], 'loss', 212)

png

评估模型

让我们看看模型的表现。将返回两个值。损失（表示误差的数字，值越低越好）和准确率。

results = model.evaluate(test_data.batch(512), verbose=2)

for name, value in zip(model.metrics_names, results):
  print('%s: %.3f' % (name, value))

4/4 - 0s - loss: 0.5406 - accuracy: 0.7805 - 293ms/epoch - 73ms/step
loss: 0.541
accuracy: 0.781

我们可以看到，损失迅速下降，而准确率则迅速上升。让我们绘制一些示例，以检查预测与真实标签之间的关系

prediction_dataset = next(iter(test_data.batch(20)))

prediction_texts = [ex.numpy().decode('utf8') for ex in prediction_dataset[0]]
prediction_labels = [label2str(x) for x in prediction_dataset[1]]

predictions = [
    label2str(x) for x in np.argmax(model.predict(prediction_texts), axis=-1)]


pd.DataFrame({
    TEXT_FEATURE_NAME: prediction_texts,
    LABEL_NAME: prediction_labels,
    'prediction': predictions
})

1/1 [==============================] - 0s 122ms/step

我们可以看到，对于这个随机样本，模型在大多数情况下预测了正确的标签，这表明它可以很好地嵌入科学句子。

下一步是什么？

现在您已经对来自 TF-Hub 的 CORD-19 Swivel 嵌入有了更多了解，我们鼓励您参与 CORD-19 Kaggle 竞赛，为从 COVID-19 相关学术文本中获取科学见解做出贡献。

参加 CORD-19 Kaggle 挑战赛
了解更多关于 COVID-19 开放研究数据集 (CORD-19)
查看 TF-Hub 嵌入的文档和更多信息，请访问 https://tfhub.dev/tensorflow/cord-19/swivel-128d/3
使用 TensorFlow 嵌入投影仪探索 CORD-19 嵌入空间