BERT Experts from TF-Hub

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This colab demonstrates how to:

  • Load BERT models from TensorFlow Hub that have been trained on different tasks including MNLI, SQuAD, and PubMed
  • Use a matching preprocessing model to tokenize raw text and convert it to ids
  • Generate the pooled and sequence output from the token input ids using the loaded model
  • Look at the semantic similarity of the pooled outputs of different sentences

Note: This colab should be run with a GPU runtime

Set up and imports

pip3 install --quiet tensorflow
pip3 install --quiet tensorflow_text
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.

Configure the model

Sentences

Let's take some sentences from Wikipedia to run through model

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.",
]

Run the model

We'll load the BERT model from TF-Hub, tokenize our sentences using the matching preprocessing model from TF-Hub, then feed in the tokenized sentences to the model. To keep this colab fast and simple, we recommend running on GPU.

Go to RuntimeChange runtime type to make sure that GPU is selected

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"])
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."]

BERT inputs:
{'input_type_ids': <tf.Tensor: shape=(9, 128), dtype=int32, numpy=
array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)>, 'input_mask': <tf.Tensor: shape=(9, 128), dtype=int32, numpy=
array([[1, 1, 1, ..., 0, 0, 0],
       [1, 1, 1, ..., 0, 0, 0],
       [1, 1, 1, ..., 0, 0, 0],
       ...,
       [1, 1, 1, ..., 0, 0, 0],
       [1, 1, 1, ..., 0, 0, 0],
       [1, 1, 1, ..., 0, 0, 0]], dtype=int32)>, 'input_word_ids': <tf.Tensor: shape=(9, 128), dtype=int32, numpy=
array([[  101,  2182,  2057, ...,     0,     0,     0],
       [  101,  1996,  2201, ...,     0,     0,     0],
       [  101,  2426,  1996, ...,     0,     0,     0],
       ...,
       [  101, 16447,  6714, ...,     0,     0,     0],
       [  101,  1037,  5943, ...,     0,     0,     0],
       [  101,  1037,  7704, ...,     0,     0,     0]], dtype=int32)>}

Pooled embeddings:
tf.Tensor(
[[ 0.79759794 -0.48580435  0.49781656 ... -0.34488496  0.39727688
  -0.20639414]
 [ 0.57120484 -0.41205186  0.70489156 ... -0.35185218  0.19032398
  -0.4041889 ]
 [-0.6993836   0.1586663   0.06569844 ... -0.06232387 -0.8155013
  -0.07923748]
 ...
 [-0.35727036  0.77089816  0.15756643 ...  0.441857   -0.8644817
   0.04504787]
 [ 0.9107702   0.41501534  0.5606339  ... -0.49263883  0.3964067
  -0.05036191]
 [ 0.90502924 -0.15505327  0.726722   ... -0.34734532  0.50526506
  -0.19542982]], shape=(9, 768), dtype=float32)

Per token embeddings:
tf.Tensor(
[[[ 1.09197533e+00 -5.30553877e-01  5.46399117e-01 ... -3.59626472e-01
    4.20411289e-01 -2.09402084e-01]
  [ 1.01438284e+00  7.80790329e-01  8.53758693e-01 ...  5.52820444e-01
   -1.12457883e+00  5.60277641e-01]
  [ 7.88627684e-01  7.77753443e-02  9.51507747e-01 ... -1.90755337e-01
    5.92060506e-01  6.19107723e-01]
  ...
  [-3.22031736e-01 -4.25212324e-01 -1.28237933e-01 ... -3.90951157e-01
   -7.90973544e-01  4.22365129e-01]
  [-3.10389847e-02  2.39855915e-01 -2.19942629e-01 ... -1.14405245e-01
   -1.26804781e+00 -1.61363974e-01]
  [-4.20636892e-01  5.49730241e-01 -3.24446023e-01 ... -1.84789032e-01
   -1.13429689e+00 -5.89773059e-02]]

 [[ 6.49309337e-01 -4.38080192e-01  8.76956999e-01 ... -3.67556065e-01
    1.92673296e-01 -4.28645700e-01]
  [-1.12487435e+00  2.99313068e-01  1.17996347e+00 ...  4.87294406e-01
    5.34003854e-01  2.28363827e-01]
  [-2.70572990e-01  3.23538631e-02  1.04257035e+00 ...  5.89937270e-01
    1.53678954e+00  5.84256709e-01]
  ...
  [-1.47624981e+00  1.82391271e-01  5.58804125e-02 ... -1.67332077e+00
   -6.73984885e-01 -7.24499583e-01]
  [-1.51381290e+00  5.81846952e-01  1.61421359e-01 ... -1.26408398e+00
   -4.02721316e-01 -9.71973777e-01]
  [-4.71531510e-01  2.28173390e-01  5.27765870e-01 ... -7.54838765e-01
   -9.09029484e-01 -1.69548154e-01]]

 [[-8.66093040e-01  1.60018250e-01  6.57932162e-02 ... -6.24047518e-02
   -1.14323711e+00 -7.94039369e-02]
  [ 7.71180928e-01  7.08045244e-01  1.13499165e-01 ...  7.88309634e-01
   -3.14380586e-01 -9.74871933e-01]
  [-4.40023899e-01 -3.00594330e-01  3.54794949e-01 ...  7.97353014e-02
   -4.73935485e-01 -1.10018420e+00]
  ...
  [-1.02053010e+00  2.69383639e-01 -4.73101676e-01 ... -6.63193762e-01
   -1.45799184e+00 -3.46655250e-01]
  [-9.70034838e-01 -4.50136065e-02 -5.97798169e-01 ... -3.05265576e-01
   -1.27442575e+00 -2.80517340e-01]
  [-7.31442988e-01  1.76993430e-01 -4.62578893e-01 ... -1.60623401e-01
   -1.63460755e+00 -3.20607185e-01]]

 ...

 [[-3.73753369e-01  1.02253771e+00  1.58890173e-01 ...  4.74535972e-01
   -1.31081581e+00  4.50783782e-02]
  [-4.15891230e-01  5.00191450e-01 -4.58438754e-01 ...  4.14822072e-01
   -6.20658875e-01 -7.15549171e-01]
  [-1.25043917e+00  5.09365320e-01 -5.71037054e-01 ...  3.54916602e-01
    2.43683696e-01 -2.05771995e+00]
  ...
  [ 1.33936703e-01  1.18591738e+00 -2.21700743e-01 ... -8.19471061e-01
   -1.67373013e+00 -3.96926820e-01]
  [-3.36624265e-01  1.65562105e+00 -3.78126293e-01 ... -9.67453301e-01
   -1.48010290e+00 -8.33311737e-01]
  [-2.26493448e-01  1.61784422e+00 -6.70443296e-01 ... -4.90783423e-01
   -1.45356917e+00 -7.17075229e-01]]

 [[ 1.53202307e+00  4.41654980e-01  6.33757174e-01 ... -5.39538860e-01
    4.19378459e-01 -5.04045524e-02]
  [ 8.93778205e-01  8.93955052e-01  3.06287408e-02 ...  5.90391904e-02
   -2.06495613e-01 -8.48110974e-01]
  [-1.85600221e-02  1.04790771e+00 -1.33295977e+00 ... -1.38697088e-01
   -3.78795475e-01 -4.90686238e-01]
  ...
  [ 1.42756522e+00  1.06969848e-01 -4.06335592e-02 ... -3.17773186e-02
   -4.14598197e-01  7.00368583e-01]
  [ 1.12866342e+00  1.45478487e-01 -6.13721192e-01 ...  4.74921733e-01
   -3.98516655e-01  4.31243867e-01]
  [ 1.43932939e+00  1.80306956e-01 -4.28539753e-01 ... -2.50225902e-01
   -1.00005007e+00  3.59855264e-01]]

 [[ 1.49934173e+00 -1.56314075e-01  9.21745181e-01 ... -3.62421691e-01
    5.56351066e-01 -1.97976440e-01]
  [ 1.11105371e+00  3.66513431e-01  3.55058551e-01 ... -5.42975247e-01
    1.44716531e-01 -3.16758066e-01]
  [ 2.40487278e-01  3.81156325e-01 -5.91827273e-01 ...  3.74107122e-01
   -5.98296165e-01 -1.01662648e+00]
  ...
  [ 1.01586223e+00  5.02603769e-01  1.07373089e-01 ... -9.56426382e-01
   -4.10394996e-01 -2.67601997e-01]
  [ 1.18489289e+00  6.54797733e-01  1.01688504e-03 ... -8.61546934e-01
   -8.80392492e-02 -3.06370854e-01]
  [ 1.26691115e+00  4.77678716e-01  6.62857294e-03 ... -1.15858066e+00
   -7.06758797e-02 -1.86787039e-01]]], shape=(9, 128, 768), dtype=float32)

Semantic similarity

Now let's take a look at the pooled_output embeddings of our sentences and compare how similar they are across sentences.

Helper functions

plot_similarity(outputs["pooled_output"], sentences)

png

Learn more