feat(encoders): add universal sentence encoder

extra-requirements.txt

@@ -28,6 +28,7 @@ paddlepaddle:               framework, py37
 paddlehub:                  framework, py37
 Pillow:                     cv
 tensorflow>=2.0:            framework, py37
+tensorflow-hub:             framework, py37
 torchvision:                framework
 onnx:                       framework, py37
 onnxruntime:                framework, py37
@@ -39,4 +40,4 @@ lz4:                        optimization, devel, production, network
 gevent:                     http, devel
 python-magic:               http, devel
 librosa:                    audio
-deepsegment:                nlp, preprocess, craft
+deepsegment:                nlp, preprocess, craft

jina/executors/encoders/nlp/use.py

@@ -0,0 +1,47 @@
+__copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved."
+__license__ = "Apache-2.0"
+
+import numpy as np
+
+from ..frameworks import BaseTextTFEncoder
+from ...decorators import batching, as_ndarray
+
+
+class UniversalSentenceEncoder(BaseTextTFEncoder):
+    """
+    :class:`UniversalSentenceEncoder` is a encoder based on the Universal Sentence
+    Encoder family (https://tfhub.dev/google/collections/universal-sentence-encoder/1).
+    It encodes data from an 1d array of string in size `B` into an ndarray in size `B x D`.
+    """
+
+    def __init__(
+            self,
+            model_url: str = 'https://tfhub.dev/google/universal-sentence-encoder/4',
+            *args,
+            **kwargs):
+        """
+        :param model_url: the url of the model (TensorFlow Hub). For supported models see
+                          family overview: https://tfhub.dev/google/collections/universal-sentence-encoder/1)
+        :param args:
+        :param kwargs:
+        """
+        super().__init__(*args, **kwargs)
+        if self.model_url is None:
+            self.model_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
+
+    def post_init(self):
+        self.to_device()
+        import tensorflow_hub as hub
+        self.model = hub.load(self.model_url)
+
+    @batching
+    @as_ndarray
+    def encode(self, data: 'np.ndarray', *args, **kwargs) -> 'np.ndarray':
+        """
+
+        :param data: a 1d array of string type in size `B`
+        :param args:
+        :param kwargs:
+        :return: an ndarray in size `B x D`
+        """
+        return self.model(data).numpy()

tests/executors/encoders/nlp/test_use.py

@@ -0,0 +1,13 @@
+import unittest
+
+from jina.executors.encoders.nlp.use import UniversalSentenceEncoder
+from tests.executors.encoders.nlp import NlpTestCase
+
+
+class UniversalSentenceEncoderTestCase(NlpTestCase):
+    def _get_encoder(self, metas):
+        return UniversalSentenceEncoder(metas=metas)
+
+
+if __name__ == '__main__':
+    unittest.main()