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TF-हब कॉर्ड -19 कुंडा एंबेडिंग की खोज

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The CORD-19 Swivel text embedding module from TF-Hub ( was built to support researchers analyzing natural languages text related to COVID-19. These embeddings were trained on the titles, authors, abstracts, body texts, and reference titles of articles in the CORD-19 dataset.

In this colab we will:

  • Analyze semantically similar words in the embedding space
  • Train a classifier on the SciCite dataset using the CORD-19 embeddings


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

import tensorflow.compat.v1 as tf

import tensorflow_datasets as tfds
import tensorflow_hub as hub

  from google.colab import data_table
  def display_df(df):
    return data_table.DataTable(df, include_index=False)
except ModuleNotFoundError:
  # If google-colab is not available, just display the raw DataFrame
  def display_df(df):
    return df

Analyze the embeddings

Let's start off by analyzing the embedding by calculating and plotting a correlation matrix between different terms. If the embedding learned to successfully capture the meaning of different words, the embedding vectors of semantically similar words should be close together. Let's take a look at some COVID-19 related terms.

# 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)

with tf.Graph().as_default():
  # Load the module
  query_input = tf.placeholder(tf.string)
  module = hub.Module('')
  embeddings = module(query_input)

  with tf.train.MonitoredTrainingSession() as sess:

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

    features =, feed_dict={query_input: queries})
    plot_correlation(queries, features)


We can see that the embedding successfully captured the meaning of the different terms. Each word is similar to the other words of its cluster (i.e. "coronavirus" highly correlates with "SARS" and "MERS"), while they are different from terms of other clusters (i.e. the similarity between "SARS" and "Spain" is close to 0).

Now let's see how we can use these embeddings to solve a specific task.

SciCite: Citation Intent Classification

This section shows how one can use the embedding for downstream tasks such as text classification. We'll use the SciCite dataset from TensorFlow Datasets to classify citation intents in academic papers. Given a sentence with a citation from an academic paper, classify whether the main intent of the citation is as background information, use of methods, or comparing results.

Set up the dataset from TFDS

Downloading and preparing dataset scicite/1.0.0 (download: 22.12 MiB, generated: Unknown size, total: 22.12 MiB) to /home/kbuilder/tensorflow_datasets/scicite/1.0.0...
Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/scicite/1.0.0.incompleteHWK5SE/scicite-train.tfrecord
Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/scicite/1.0.0.incompleteHWK5SE/scicite-validation.tfrecord
Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/scicite/1.0.0.incompleteHWK5SE/scicite-test.tfrecord
Dataset scicite downloaded and prepared to /home/kbuilder/tensorflow_datasets/scicite/1.0.0. Subsequent calls will reuse this data.

Let's take a look at a few labeled examples from the training set

Training a citaton intent classifier

We'll train a classifier on the SciCite dataset using an Estimator. Let's set up the input_fns to read the dataset into the model

def preprocessed_input_fn(for_eval):
  data = THE_DATASET.get_data(for_eval=for_eval)
  data =, num_parallel_calls=1)
  return data

def input_fn_train(params):
  data = preprocessed_input_fn(for_eval=False)
  data = data.repeat(None)
  data = data.shuffle(1024)
  data = data.batch(batch_size=params['batch_size'])
  return data

def input_fn_eval(params):
  data = preprocessed_input_fn(for_eval=True)
  data = data.repeat(1)
  data = data.batch(batch_size=params['batch_size'])
  return data

def input_fn_predict(params):
  data = preprocessed_input_fn(for_eval=True)
  data = data.batch(batch_size=params['batch_size'])
  return data

Let's build a model which use the CORD-19 embeddings with a classification layer on top.

def model_fn(features, labels, mode, params):
  # Embed the text
  embed = hub.Module(params['module_name'], trainable=params['trainable_module'])
  embeddings = embed(features['feature'])

  # Add a linear layer on top
  logits = tf.layers.dense(
      embeddings, units=THE_DATASET.num_classes(), activation=None)
  predictions = tf.argmax(input=logits, axis=1)

  if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(
            'logits': logits,
            'predictions': predictions,
            'features': features['feature'],
            'labels': features['label']

  # Set up a multi-class classification head
  loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
      labels=labels, logits=logits)
  loss = tf.reduce_mean(loss)

  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=params['learning_rate'])
    train_op = optimizer.minimize(loss, global_step=tf.train.get_or_create_global_step())
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

  elif mode == tf.estimator.ModeKeys.EVAL:
    accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions)
    precision = tf.metrics.precision(labels=labels, predictions=predictions)
    recall = tf.metrics.recall(labels=labels, predictions=predictions)

    return tf.estimator.EstimatorSpec(
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,


Train and evaluate the model

Let's train and evaluate the model to see the performance on the SciCite task

estimator = tf.estimator.Estimator(functools.partial(model_fn, params=params))
metrics = []

for step in range(0, STEPS, EVAL_EVERY):
  estimator.train(input_fn=functools.partial(input_fn_train, params=params), steps=EVAL_EVERY)
  step_metrics = estimator.evaluate(input_fn=functools.partial(input_fn_eval, params=params))
  print('Global step {}: loss {:.3f}, accuracy {:.3f}'.format(step, step_metrics['loss'], step_metrics['accuracy']))
Global step 0: loss 0.796, accuracy 0.670
Global step 200: loss 0.701, accuracy 0.732
Global step 400: loss 0.682, accuracy 0.719
Global step 600: loss 0.650, accuracy 0.747
Global step 800: loss 0.620, accuracy 0.762
Global step 1000: loss 0.609, accuracy 0.762
Global step 1200: loss 0.605, accuracy 0.762
Global step 1400: loss 0.585, accuracy 0.783
Global step 1600: loss 0.586, accuracy 0.768
Global step 1800: loss 0.577, accuracy 0.774
Global step 2000: loss 0.584, accuracy 0.765
Global step 2200: loss 0.565, accuracy 0.778
Global step 2400: loss 0.570, accuracy 0.776
Global step 2600: loss 0.556, accuracy 0.789
Global step 2800: loss 0.563, accuracy 0.778
Global step 3000: loss 0.557, accuracy 0.784
Global step 3200: loss 0.566, accuracy 0.774
Global step 3400: loss 0.552, accuracy 0.782
Global step 3600: loss 0.551, accuracy 0.785
Global step 3800: loss 0.547, accuracy 0.788
Global step 4000: loss 0.549, accuracy 0.784
Global step 4200: loss 0.548, accuracy 0.785
Global step 4400: loss 0.553, accuracy 0.783
Global step 4600: loss 0.543, accuracy 0.786
Global step 4800: loss 0.548, accuracy 0.783
Global step 5000: loss 0.547, accuracy 0.785
Global step 5200: loss 0.539, accuracy 0.791
Global step 5400: loss 0.546, accuracy 0.782
Global step 5600: loss 0.548, accuracy 0.781
Global step 5800: loss 0.540, accuracy 0.791
Global step 6000: loss 0.542, accuracy 0.790
Global step 6200: loss 0.539, accuracy 0.792
Global step 6400: loss 0.545, accuracy 0.788
Global step 6600: loss 0.552, accuracy 0.781
Global step 6800: loss 0.549, accuracy 0.783
Global step 7000: loss 0.540, accuracy 0.788
Global step 7200: loss 0.543, accuracy 0.782
Global step 7400: loss 0.541, accuracy 0.787
Global step 7600: loss 0.532, accuracy 0.790
Global step 7800: loss 0.537, accuracy 0.792
global_steps = [x['global_step'] for x in metrics]
fig, axes = plt.subplots(ncols=2, figsize=(20,8))

for axes_index, metric_names in enumerate([['accuracy', 'precision', 'recall'],
  for metric_name in metric_names:
    axes[axes_index].plot(global_steps, [x[metric_name] for x in metrics], label=metric_name)
  axes[axes_index].set_xlabel("Global Step")


We can see that the loss quickly decreases while especially the accuracy rapidly increases. Let's plot some examples to check how the prediction relates to the true labels:

predictions = estimator.predict(functools.partial(input_fn_predict, params))
first_10_predictions = list(itertools.islice(predictions, 10))

      TEXT_FEATURE_NAME: [pred['features'].decode('utf8') for pred in first_10_predictions],
      LABEL_NAME: [THE_DATASET.class_names()[pred['labels']] for pred in first_10_predictions],
      'prediction': [THE_DATASET.class_names()[pred['predictions']] for pred in first_10_predictions]

We can see that for this random sample, the model predicts the correct label most of the times, indicating that it can embed scientific sentences pretty well.

What's next?

Now that you've gotten to know a bit more about the CORD-19 Swivel embeddings from TF-Hub, we encourage you to participate in the CORD-19 Kaggle competition to contribute to gaining scientific insights from COVID-19 related academic texts.