load_model.py

#%%

import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_text as text

#%%

preprocessor = hub.KerasLayer(
    "models/bert_preprocessor",)
encoder = hub.KerasLayer(
    "models/bert_encoder",
    trainable=True)

#%%

def get_bert_embeddings(text, preprocessor, encoder):
    text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
	preprocessor = hub.KerasLayer("https://kaggle.com/models/tensorflow/bert/frameworks/TensorFlow2/variations/en-uncased-preprocess/versions/3")
	encoder_inputs = preprocessor(text_input)
	encoder = hub.KerasLayer("https://www.kaggle.com/models/tensorflow/bert/frameworks/TensorFlow2/variations/bert-en-uncased-l-10-h-128-a-2/versions/2",
    trainable=True)
	outputs = encoder(encoder_inputs)
    embedding_model = tf.keras.Model(text_input, outputs['pooled_output'])
    embedding_model.compile(optimizer='adam', loss='mse')
    sentences = tf.constant([text])
    emb = embedding_model(sentences).numpy()
    print(emb)
    return emb

#%
#%%
# def get_bert_embeddings(text):
#     preprocessor, encoder = load_models()
#     print(preprocessor)
#     text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
#     encoder_inputs = preprocessor(text_input)
#     print(encoder_inputs)
#     outputs = encoder(encoder_inputs)
#     embedding_model = tf.keras.Model(text_input, outputs['pooled_output'])
#     embedding_model.compile(optimizer='adam', loss='mse')
#     sentences = tf.constant([text])
#     emb = embedding_model(sentences).numpy()
#     return emb

def get_bert_embeddings(text):
    text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
    preprocessor = hub.KerasLayer("https://kaggle.com/models/tensorflow/bert/frameworks/TensorFlow2/variations/en-uncased-preprocess/versions/3")
    encoder_inputs = preprocessor(text_input)
    encoder = hub.KerasLayer("https://www.kaggle.com/models/tensorflow/bert/frameworks/TensorFlow2/variations/bert-en-uncased-l-10-h-128-a-2/versions/2", trainable=True)
    outputs = encoder(encoder_inputs)
    embedding_model = tf.keras.Model(text_input, outputs['pooled_output'])
    embedding_model.compile(optimizer='adam', loss='mse')
    sentences = tf.constant([text])
    emb = embedding_model(sentences).numpy()
    print(emb)
    return emb


def preprocess_text(text):
    text = text.lower()
    text = re.sub('[^A-Za-z0-9]+', ' ', text)
    return text
#%%

from blog.models import Post

posts = Post.objects.all()
encodings = posts.values_list('encodings', flat=True)

#%%

query_text = "hello barcelona"
query_encoding = get_bert_embeddings(query_text)
#%%

embeddings = [[-0.88768613  0.9936917  -0.01275343 -0.92733675 -0.99101025 -0.21298912
  -0.93074405  0.04495206  0.9523757  -0.64787173 -0.92024606  0.90312046
   0.9337168   0.9237844  -0.03015616  0.6809516  -0.9721272   0.49882787
  -0.9997883   0.7853903  -0.8452043  -0.5616959  -0.5770693   0.94836575
  -0.77471733  0.9291275  -0.03696804 -0.99905276 -0.0215473  -0.9128285
   0.99681896  0.98527193  0.94743127  0.8676614   0.926444    0.01454866
  -0.97729367 -0.26520723 -0.14627674  0.6367502  -0.98087907 -0.77433234
   0.09468498  0.99762595 -0.9081744  -0.9642555   0.11682082 -0.6107718
   0.8864423   0.99533165 -0.55286103 -0.89173806  0.692388   -0.70267403
   0.00996532 -0.32953182  0.08960655  0.08331505 -0.9950165   0.86567247
  -0.9945199  -0.729069    0.02909042 -0.02385897 -0.73677874  0.9781143
   0.00809213 -0.9431931  -0.0150731  -0.8647785  -0.99697274  0.02440747
  -0.7598452  -0.04904811  0.9998987  -0.71627307  0.04297871 -0.09828732
   0.47461966  0.0976806   0.108974    0.9804444  -0.5876865   0.89506525
   0.529461    0.8388677   0.11936213  0.10458349 -0.6120341  -0.02503585
   0.21232718  0.99561524 -0.99121976  0.5576346  -0.82864535 -0.9654387
   0.39845455  0.65060306 -0.11256316 -0.06756154 -0.489061   -0.9720269
  -0.9850079  -0.92635334  0.99930775  0.99983263  0.16510658 -0.97066045
  -0.25763205 -0.74733955  0.9979774  -0.9910868   0.818432    0.84936863
  -0.9832708  -0.9662415  -0.7076529  -0.00578223 -0.8486959  -0.72619987
   0.71836156 -0.9750119  -0.8990578   0.04990553 -0.75771755  0.99073404
  -0.33662575  0.02989081]]
[[-7.13601649e-01  9.07809615e-01  3.15431878e-02 -9.47138727e-01
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  -4.22307104e-01 -4.98149782e-01 -7.95411348e-01 -9.94664848e-01
   9.56931472e-01  9.97495949e-01  2.05630049e-01 -8.84599149e-01
   3.86949122e-01 -8.36145341e-01  9.95876431e-01  6.27484977e-01
   2.40575433e-01  6.98803544e-01 -5.53883791e-01 -9.94238377e-01
  -9.54716921e-01 -5.62251866e-01  2.64733791e-01 -5.10053098e-01
  -8.18359375e-01 -4.86190170e-01  2.40690842e-01  5.10892682e-02
   6.87513828e-01  9.79677498e-01 -5.79140604e-01  1.29730478e-01]]
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  -0.92524976 -0.77806675 -0.76991904 -0.825105   -0.50049496 -0.7113621
   0.33789712 -0.56866616 -0.8281851   0.11836787  0.39915732  0.972371
  -0.59215474  0.16069406]]
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   0.7441065   0.03281637  0.5623518   0.9727372  -0.6937819   0.82275283
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   0.07423585 -0.68202376 -0.8099639   0.06755771 -0.45313856  0.96581733
  -0.3574603   0.0767763 ]]
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   0.49556708  0.0734164 ]]
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