Add python api wrappers over sentence and tokens.

README.md

@@ -67,7 +67,7 @@ Works for models where input was text-based only.
 Interactive testing in console (load model and just type sentence in console).
 
 ```bash
-combo --mode predict --model_path your_model_tar_gz --input_file "-"
+combo --mode predict --model_path your_model_tar_gz --input_file "-" --nosilent
 ```
 ### Raw text
 Works for models where input was text-based only. 
@@ -91,7 +91,7 @@ import combo.predict as predict
 
 model_path = "your_model.tar.gz"
 nlp = predict.SemanticMultitaskPredictor.from_pretrained(model_path)
-parsed_tree = nlp("Sentence to parse.")["tree"]
+sentence = nlp("Sentence to parse.")
 ```
 
 ## Configuration

combo/data/__init__.py

 from .samplers import TokenCountBatchSampler
 from .token_indexers import TokenCharactersIndexer
+from .api import *

combo/data/api.py

+from typing import Optional, List
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class Token:
+    token: Optional[str] = None
+    id: Optional[int] = None
+    lemma: Optional[str] = None
+    upostag: Optional[str] = None
+    xpostag: Optional[str] = None
+    head: Optional[int] = None
+    deprel: Optional[str] = None
+    feats: Optional[str] = None
+
+    @staticmethod
+    def from_json(json):
+        return Token(**json)
+
+
+@dataclass
+class Sentence:
+    tokens: List[Token] = field(default_factory=list)
+    embedding: List[float] = field(default_factory=list)
+
+    @staticmethod
+    def from_json(json):
+        return Sentence(tokens=[Token.from_json(t) for t in json['tree']],
+                        embedding=json.get('sentence_embedding', []))
+

combo/data/dataset.py

@@ -86,7 +86,7 @@ class UniversalDependenciesDatasetReader(allen_data.DatasetReader):
     @overrides
     def text_to_instance(self, tree: conllu.TokenList) -> allen_data.Instance:
         fields_: Dict[str, allen_data.Field] = {}
-        tokens = [Token(t['token'],
+        tokens = [_Token(t['token'],
                         pos_=t.get('upostag'),
                         tag_=t.get('xpostag'),
                         lemma_=t.get('lemma'),
@@ -233,5 +233,5 @@ def get_slices_if_not_provided(vocab: allen_data.Vocabulary):
 
 
 @dataclass
-class Token(allen_data.Token):
+class _Token(allen_data.Token):
     feats_: Optional[str] = None

combo/predict.py

@@ -10,6 +10,8 @@ from allennlp.data import tokenizers
 from allennlp.predictors import predictor
 from overrides import overrides
 
+from combo import data
+
 logger = logging.getLogger(__name__)
 
 
@@ -58,7 +60,7 @@ class SemanticMultitaskPredictor(predictor.Predictor):
         return result
 
     def predict(self, sentence: str):
-        return self.predict_json({'sentence': sentence})
+        return data.Sentence.from_json(self.predict_json({'sentence': sentence}))
 
     def __call__(self, sentence: str):
         return self.predict(sentence)