Support embeddings and semantic-similarity endpoints.

Author: Julien Salinas

README.md

@@ -140,7 +140,6 @@ Call the `generation()` method and pass the following arguments:
 1. (Optional) `length_penalty`: Exponential penalty to the length, as a float. 1.0 means no penalty. Set to values < 1.0 in order to encourage the model to generate shorter sequences, or to a value > 1.0 in order to encourage the model to produce longer sequences. Defaults to 1.0.
 1. (Optional) `bad_words`: List of tokens that are not allowed to be generated, as a list of strings. Defaults to null.
 
-
 ```ruby
 client.generation("<Your input text>")
 ```
@@ -200,6 +199,16 @@ client.langdetection("<The text you want to analyze>")
 
 The above command returns a JSON object.
 
+### Semantic Similarity Endpoint
+
+Call the `semantic_similarity()` method and pass a list made up of 2 blocks of text that you want to compare.
+
+```python
+client.semantic_similarity(["<Block of text 1>", "<Block of text 2>")
+```
+
+The above command returns a JSON object.
+
 ### Tokenization Endpoint
 
 Call the `tokens()` method and pass the text you want to tokenize.
@@ -230,6 +239,16 @@ client.sentence_dependencies("<Your block of text>")
 
 The above command returns a JSON object.
 
+### Embeddings Endpoint
+
+Call the `embeddings()` method and pass a list of blocks of text that you want to extract embeddings from.
+
+```ruby
+client.embeddings(["<Text 1>", "<Text 2>", "<Tex 3>"], ...)
+```
+
+The above command returns a JSON object.
+
 ### Library Versions Endpoint
 
 Call the `lib_versions()` method to know the versions of the libraries used behind the hood with the model (for example the PyTorch, TensorFlow or spaCy versions used).
@@ -239,4 +258,3 @@ client.lib_versions()
 ```
 
 The above command returns a JSON object.
-

lib/nlpcloud.rb

@@ -114,6 +114,14 @@ def langdetection(text)
       JSON.parse(response.body)
     end
 
+    def semantic_similarity(sentences)
+      payload = {
+        'sentences' => sentences
+      }
+      response = RestClient.post("#{@root_url}/semantic_similarity", payload.to_json, @headers)
+      JSON.parse(response.body)
+    end
+
     def tokens(text)
       payload = {
         'text' => text
@@ -138,6 +146,14 @@ def sentence_dependencies(text)
       JSON.parse(response.body)
     end
 
+    def embeddings(sentences)
+      payload = {
+        'sentences' => sentences
+      }
+      response = RestClient.post("#{@root_url}/embeddings", payload.to_json, @headers)
+      JSON.parse(response.body)
+    end
+
     def lib_versions
       response = RestClient.get("#{@root_url}/versions", @headers)
       JSON.parse(response.body)

nlpcloud.gemspec

@@ -1,8 +1,8 @@
 Gem::Specification.new do |s|
   s.name = 'nlpcloud'
-  s.version     = '1.0.16'
+  s.version     = '1.0.17'
   s.summary     = 'Ruby client for the NLP Cloud API'
-  s.description = 'NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, text generation, question answering, machine translation, language detection, tokenization, POS tagging, and dependency parsing. It is ready for production, served through a REST API. This is the Ruby client for the API. More details here: https://nlpcloud.io. Documentation: https://docs.nlpcloud.io.'
+  s.description = 'NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, text generation, question answering, machine translation, language detection, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. This is the Ruby client for the API. More details here: https://nlpcloud.io. Documentation: https://docs.nlpcloud.io.'
   s.authors     = ['Julien Salinas']
   s.email       = 'all@juliensalinas.com'
   s.files       = [