resource and dependency cleanup

src/infer.py

@@ -33,8 +33,6 @@ def score(task):
         return Inferencer.load(_dt.get_path('model_dir'))   
     elif task_type == 'ner':
         return ner.NER(task=task, inference=True)
-    elif task_type == 'om':
-        return om.OM(task=task, inference=True)
     elif task_type == 'qa':
         return rank.Rank(task=task, inference=True)
     else:

src/summarization.py

@@ -1,46 +0,0 @@
-from summarizer import Summarizer
-import nltk
-nltk.download('punkt')
-import re
-from nltk.tokenize import sent_tokenize
-from nltk.corpus import stopwords
-from sklearn.metrics.pairwise import cosine_similarity
-from gensim.summarization.summarizer import summarize
-import networkx as nx
-import numpy as np
-
-""" BERTABS """
-def summarizeText(text, minLength=60):
-    result = model(text, min_length = minLength)
-    full = ''.join(result)
-    return full
-
-""" SAMPLING """
-def sentencenize(text):
-    sentences = []
-    for sent in text:
-        sentences.append(sent_tokenize(sent))
-    sentences = [y for x in sentences for y in x]
-    return sentences
-
-def extractWordVectors(file):
-    word_embeddings = {}
-    for line in file:
-        values = line.split()
-        word = values[0]
-        coefs = np.asarray(values[1:], dtype='float32')
-        word_embeddings[word] = coefs
-    file.close()
-
-def removeStopwords(sen, sw):
-    sentence = " ".join([i for i in sen if i not in sw])
-    return sentence
-
-""" BERTABS """
-model = Summarizer()
-
-""" SAMPLING """
-clean_sentences = [removeStopwords(r.split(), sw) for r in clean_sentences]
-
-""" GENSIM """
-summarize()