query embeddings using glove #273
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Adding one hot encoding for query length
| self.assertIn(str(word2), query_embedding_vector.word_vectors) | ||
| self.assertEqual(len(query_embedding_vector.word_vectors), 2) | ||
| self.assertEqual(query_embedding_vector.embedding_dim, 3) | ||
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@mohazahran these asserts/tests could be separate unit tests or subtests to improve readability.
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| # Filter out stopwords | ||
| # def filter_stopwords(token): | ||
| # return tf.logical_not(tf.reduce_any(tf.equal(token, list(self.stop_words)))) | ||
| # | ||
| # mask = tf.map_fn(filter_stopwords, tokens, fn_output_signature=tf.bool) | ||
| # tokens = tf.ragged.boolean_mask(tokens, mask) |
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@mohazahran clean up
| @@ -158,3 +171,179 @@ def call(self, inputs, training=None): | |||
| query_type_vector = self.categorical_vector_op(query_type, training=training) | |||
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| return query_type_vector | |||
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| class QueryEmbeddingVector(BaseFeatureLayerOp): | |||
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@mohazahran I encourage you to define it as a feature layer instead of feature fn and then have a wrapper feature fn. This allows the layer to be used in model config as well and is more customizable.
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