020: Project restructuring

Author: TharinduDR

setup.py

@@ -5,7 +5,7 @@
 
 setup(
  name="simplests",
- version="2.1.1",
+ version="2.1.2",
  author="Tharindu Ranasinghe",
  author_email="rhtdranasinghe@gmail.com",
  description="Unsupervised models for Semantic Textual Similarity",

simplests/algo/sif.py

@@ -3,7 +3,7 @@
 import numpy as np
 from flair.data import Sentence
 from flair.embeddings import StackedEmbeddings, WordEmbeddings, CharacterEmbeddings, TransformerWordEmbeddings, \
- ELMoEmbeddings
+ ELMoEmbeddings, FastTextEmbeddings
 from numpy import dot
 from numpy.linalg import norm
 from tqdm import tqdm
@@ -35,6 +35,8 @@ def __init__(self, model_args: WordEmbeddingSTSArgs):
  for model_type, model_name in self.model_args.embedding_models:
  if model_type == "word":
  embedding_models.append(WordEmbeddings(model_name))
+ elif model_type == "fasttext":
+ embedding_models.append(FastTextEmbeddings(model_name))
  elif model_type == "char":
  embedding_models.append(CharacterEmbeddings(model_name))
  elif model_type == "elmo":

simplests/algo/wmd.py

@@ -3,7 +3,7 @@
 import numpy as np
 from flair.data import Sentence
 from flair.embeddings import StackedEmbeddings, WordEmbeddings, CharacterEmbeddings, TransformerWordEmbeddings, \
- ELMoEmbeddings
+ ELMoEmbeddings, FastTextEmbeddings
 from numpy import dot
 from numpy.linalg import norm
 from tqdm import tqdm
@@ -41,6 +41,8 @@ def __init__(self, model_args: WordEmbeddingSTSArgs):
  for model_type, model_name in self.model_args.embedding_models:
  if model_type == "word":
  embedding_models.append(WordEmbeddings(model_name))
+ elif model_type == "fasttext":
+ embedding_models.append(FastTextEmbeddings(model_name))
  elif model_type == "char":
  embedding_models.append(CharacterEmbeddings(model_name))
  elif model_type == "elmo":

simplests/algo/word_avg.py

@@ -3,7 +3,7 @@
 import numpy as np
 from flair.data import Sentence
 from flair.embeddings import StackedEmbeddings, WordEmbeddings, CharacterEmbeddings, TransformerWordEmbeddings, \
- ELMoEmbeddings
+ ELMoEmbeddings, FastTextEmbeddings
 from numpy import dot
 from numpy.linalg import norm
 from tqdm import tqdm
@@ -26,6 +26,8 @@ def __init__(self, model_args: WordEmbeddingSTSArgs):
  for model_type, model_name in self.model_args.embedding_models:
  if model_type == "word":
  embedding_models.append(WordEmbeddings(model_name))
+ elif model_type == "fasttext":
+ embedding_models.append(FastTextEmbeddings(model_name))
  elif model_type == "char":
  embedding_models.append(CharacterEmbeddings(model_name))
  elif model_type == "elmo":