def test_component_similarity(doc):
s2v = Sense2VecComponent(doc.vocab, shape=(4, 4))
s2v.first_run = False
vector = numpy.asarray([4, 2, 2, 2], dtype=numpy.float32)
s2v.s2v.add("hello|INTJ", vector)
s2v.s2v.add("world|NOUN", vector)
doc = s2v(doc)
assert doc[0]._.s2v_similarity(doc[1]) == 1.0
assert doc[1:3]._.s2v_similarity(doc[1:3]) == 1.0
def test_component_to_from_bytes(doc):
s2v = Sense2VecComponent(doc.vocab, shape=(1, 4))
s2v.first_run = False
vector = numpy.asarray([4, 2, 2, 2], dtype=numpy.float32)
s2v.s2v.add("world|NOUN", vector)
assert "world|NOUN" in s2v.s2v
assert "world|GPE" not in s2v.s2v
doc = s2v(doc)
assert doc[0]._.in_s2v is False
assert doc[1]._.in_s2v is True
s2v_bytes = s2v.to_bytes()
new_s2v = Sense2VecComponent(doc.vocab).from_bytes(s2v_bytes)
new_s2v.first_run = False
assert "world|NOUN" in new_s2v.s2v
assert numpy.array_equal(new_s2v.s2v["world|NOUN"], vector)
assert "world|GPE" not in new_s2v.s2v
new_s2v.s2v.vectors.resize((2, 4))
new_s2v.s2v.add("hello|INTJ", vector)
assert doc[0]._.in_s2v is False
new_doc = new_s2v(doc)
assert new_doc[0]._.in_s2v is True
def test_component_lemmatize(doc):
lookups = doc.vocab.lookups.add_table("lemma_lookup")
lookups["world"] = "wrld"
s2v = Sense2VecComponent(doc.vocab, shape=(4, 4), lemmatize=True)
s2v.first_run = False
vector = numpy.asarray([4, 2, 2, 2], dtype=numpy.float32)
s2v.s2v.add("hello|INTJ", vector)
s2v.s2v.add("world|NOUN", vector)
s2v.s2v.add("wrld|NOUN", vector)
doc = s2v(doc)
assert doc[0]._.s2v_key == "hello|INTJ"
assert doc[1].lemma_ == "wrld"
assert doc[1]._.s2v_key == "wrld|NOUN"
lookups["hello"] = "hll"
assert doc[0].lemma_ == "hll"
assert doc[0]._.s2v_key == "hello|INTJ"
s2v.s2v.add("hll|INTJ", vector)
assert doc[0]._.s2v_key == "hll|INTJ"
new_s2v = Sense2VecComponent().from_bytes(s2v.to_bytes())
assert new_s2v.s2v.cfg["lemmatize"] is True
doc.vocab.lookups.remove_table("lemma_lookup")
def test_component_attributes(doc):
s2v = Sense2VecComponent(doc.vocab, shape=(10, 4))
vector = numpy.asarray([4, 2, 2, 2], dtype=numpy.float32)
s2v.s2v.add("world|NOUN", vector, 123)
doc = s2v(doc)
assert doc[0]._.s2v_key == "hello|INTJ"
assert doc[1]._.s2v_key == "world|NOUN"
assert doc[0]._.in_s2v is False
assert doc[1]._.in_s2v is True
assert doc[0]._.s2v_freq is None
assert doc[1]._.s2v_freq == 123
assert numpy.array_equal(doc[1]._.s2v_vec, vector)
def test_component_attributes_ents(doc):
s2v = Sense2VecComponent(doc.vocab, shape=(10, 4))
s2v.first_run = False
vector = numpy.asarray([4, 2, 2, 2], dtype=numpy.float32)
s2v.s2v.add("world|NOUN", vector)
s2v.s2v.add("world|GPE", vector)
doc = s2v(doc)
assert len(doc._.s2v_phrases) == 0
doc.ents = [Span(doc, 1, 2, label="GPE")]
assert len(doc._.s2v_phrases) == 1
phrase = doc._.s2v_phrases[0]
assert phrase._.s2v_key == "world|GPE"
assert phrase[0]._.s2v_key == "world|NOUN"
assert phrase._.in_s2v is True
assert phrase[0]._.in_s2v is True
def test_component_lemmatize(doc):
def lemmatize(doc, lookups):
for token in doc:
token.lemma_ = lookups.get(token.text, token.text)
return doc
s2v = Sense2VecComponent(doc.vocab, shape=(4, 4), lemmatize=True)
s2v.first_run = False
vector = numpy.asarray([4, 2, 2, 2], dtype=numpy.float32)
s2v.s2v.add("hello|INTJ", vector)
s2v.s2v.add("world|NOUN", vector)
s2v.s2v.add("wrld|NOUN", vector)
doc = lemmatize(doc, {"world": "wrld"})
doc = s2v(doc)
assert doc[0]._.s2v_key == "hello|INTJ"
assert doc[1].lemma_ == "wrld"
assert doc[1]._.s2v_key == "wrld|NOUN"
doc = lemmatize(doc, {"hello": "hll"})
assert doc[0].lemma_ == "hll"
assert doc[0]._.s2v_key == "hello|INTJ"
s2v.s2v.add("hll|INTJ", vector)
assert doc[0]._.s2v_key == "hll|INTJ"
new_s2v = Sense2VecComponent().from_bytes(s2v.to_bytes())
assert new_s2v.s2v.cfg["lemmatize"] is True
def __init__(self):
self.nlp = spacy.load("en_core_web_lg")
s2v = Sense2VecComponent('/path/to/reddit_vectors-1.1.0')
self.nlp.add_pipe(s2v)
self.elements = {}
with open('ai_chatbot/scripts/QuestionDomain/csv/TAG_ELEMENT.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
for row in csv_reader:
self.elements[row[0]] = row[1:-1]
self.ordinal = {}
with open('ai_chatbot/scripts/QuestionDomain/csv/TAG_ORDINAL.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
for row in csv_reader:
self.ordinal[row[0]] = int(row[1])
import spacy
import numpy as np
import sense2vec
from sense2vec import Sense2VecComponent
nlp = spacy.load('en_core_web_lg')
s2v = Sense2VecComponent()
nlp.add_pipe(s2v)
doc = nlp("A sentence about natural language processing.")
most_similar = doc[3:6]._.s2v_most_similar(3)
print(most_similar)
import spacy
from spacy.matcher import Matcher
from spacy.tokens import Doc, Span
from sense2vec import Sense2VecComponent
nlp = spacy.load("en_core_web_lg")
s2v = Sense2VecComponent(nlp.vocab).from_disk("C:/fyp/s2v_reddit_2019_lg")
nlp.add_pipe(s2v)
with open("api/v0/v01/input_list/input_list.txt", "r", encoding="utf-8") as f:
TEXT = f.read()
doc = nlp(TEXT)
def list_of_files(doc):
#find all first letters in the doc and create only unique a list of unique text file names to use
text_files = list()
for token in doc:
if token.text.isalpha():
text_files.append("text" + (str(token.text[0])).lower() + ".txt")
return set(text_files)
def find_all_begining_with(doc):
#take your entire vocab and find all words in it that begin with the same letter that each of the text_files (created in list_of_files) end with
# ..and create each text file and populate it with its own vocab
with open("api/v0/v01/vocab/vocab.txt", "r", encoding="utf-8") as f:
vocab_list = list(f.readlines())
#vocab_list =["ant", "apple", "arch", "arm", "army", "baby", "bag", "ball", "band", "basin", "basket", "bath", "bed", "bee", "bell", "berry", "bird", "blade", "board", "boat", "bone", "book", "boot", "bottle", "box", "boy", "brain", "brake", "branch", "brick", "bridge", "brush", "bucket", "bulb", "button", "cake", "camera","card", "cart", "carriage", "cat", "chain", "cheese", "chest", "chin", "church", "circle", "clock", "cloud", "coat", "collar", "comb", "cord", "cow", "cup", "curtain", "cushion", "dog", "door", "drain", "drawer", "dress", "drop", "ear", "egg", "engine", "eye", "face", "farm", "feather", "finger", "fish", "flag", "floor", "fly", "foot", "fork", "fowl", "frame", "garden", "girl", "glove", "goat", "gun", "hair", "hammer", "hand", "hat", "head", "heart", "hook", "horn", "horse", "hospital", "house", "island", "jewel", "kettle", "key", "knee", "knife", "knot", "leaf", "leg", "library", "line", "lip", "lock", "map", "match", "monkey", "moon", "mouth", "muscle", "nail", "neck", "needle", "nerve", "net", "nose", "nut", "office", "orange", "oven", "parcel", "pen", "pencil", "picture", "pig", "pin", "pipe", "plane", "plate", "plough", "pocket", "pot", "potato", "prison", "pump", "rail", "rat", "receipt", "ring", "rod", "roof", "root", "sail", "school", "scissors", "screw", "seed", "sheep", "shelf", "ship", "shoe", "skin", "snake", "sock", "spade", "sponge", "spoon", "spring", "square", "stamp", "star", "station", "stem", "stick", "stocking", "stomach", "store", "street", "sun", "table", "tail", "thread", "throat", "thumb", "ticket", "toe", "tongue", "tooth", "town", "train", "tray", "tree", "trousers", "umbrella", "wall", "watch", "wheel", "whip", "whistle", "window", "wing", "wire", "worm"]
text_files = list_of_files(doc)
for file in text_files:
with open(("api/v0/v01/textfiles/" + file), "w+",
for line in f:
values = line.split()
token = values[0]
vector = np.asarray(values[1:], "float32")
embeddings_dict[token] = vector
#Loading Word2Vec Embeddings
model = gensim.models.KeyedVectors.load_word2vec_format('/content/drive/My Drive/GoogleNews-vectors-negative300.bin.gz', binary=True)
norm_model = gensim.models.KeyedVectors.load_word2vec_format('/content/drive/My Drive/GoogleNews-vectors-negative300.bin.gz', binary=True)
norm_model.init_sims(replace=True)
#Loading Sense Embeddings
nlp = en_core_web_lg.load()
s2v = Sense2VecComponent(nlp.vocab).from_disk("/content/drive/My Drive/s2v_reddit_2019_lg")
nlp.add_pipe(s2v)
#Load training data
df = pd.read_csv('train_tsv.tsv', sep='\t', names=["is_duplicate", "question1", "question2", "id"])
df = df.set_index('id')
df = df[(df['question1'].isna() == False) & (df['question2'].isna() == False)]
training_data = list(df['question1'])+list(df['question2'])
training_data = [preprocessing_pipeline(i) for i in training_data]
tfidf_vectorizer = tfidf(training_data)
vectorizer_bow = bag_of_words(training_data, 0)
vectorizer_ngram = bag_of_words(training_data, 1)
vectorizer_3gram = trigram(training_data)
noun_chunks_df.loc[i, 'root'] = chunk.root,
noun_chunks_df.loc[i, 'root.text'] = chunk.root.text,
noun_chunks_df.loc[i, 'root.dep_'] = chunk.root.dep_
noun_chunks_df.loc[i, 'root.head.text'] = chunk.root.head.text
print(noun_chunks_df[:20])
nlp = spacy.load('en_core_web_sm', disable_pipes=["tagger", "ner"])
train_df['parsed'] = train_df.Text[49500:50500].apply(nlp)
corpus = st.CorpusFromParsedDocuments(train_df[49500:50500],
category_col='Score',
parsed_col='parsed').build()
from sense2vec.vectors import VectorMap
s2v = Sense2VecComponent('data/reddit_vectors-1.1.0/reddit_vectors-1.1.0')
spacy_tok.add_pipe(s2v)
doc = spacy_tok(u"dessert.")
freq = doc[0]._.s2v_freq
vector = doc[0]._.s2v_vec
most_similar = doc[0]._.s2v_most_similar(5)
print(most_similar, freq)
doc = spacy_tok(u"burger")
most_similar = doc[0]._.s2v_most_similar(4)
print(most_similar)
train_df['tuples'] = train_df.apply(lambda row: (row['Text'], row['Score']),
axis=1)
train = train_df['tuples'].tolist()
print(train[:1])
def tokenize(self):
if self.texts is None:
if not self.context:
# Read in text data from text_file path
self.texts = open(self.text_file).read().split('\n')
self.texts = [str(t) for t in self.texts]
print('Made texts')
else:
filename, file_ext = os.path.splitext(self.text_file)
if file_ext == '.json':
# Read in json data as dataframe
# noinspection PyUnresolvedReferences
df = pd.read_json(self.text_file, lines=True)
else:
# Read in tabular data as dataframe
# noinspection PyUnresolvedReferences
df = pd.read_csv(self.text_file,
sep=self.sep,
usecols=self.use_cols)
# Extract the text
text_col_name = self.use_cols[0]
self.texts = df[text_col_name].values.astype(str).tolist()
# Small memory reduction by deleting this
del df[text_col_name]
self.context_df = df
# Get number of documents supplied
self.n_docs = len(self.texts)
# Init data as a bunch of zeros - shape [n_docs, max_len]
self.data = np.zeros((self.n_docs, self.max_len), dtype=np.uint64)
if not self.tokenizing_new:
# Add the skip token to the vocab, creating a unique hash for it
self.nlp.vocab.strings.add(self.skip_token)
self.skip_token = self.nlp.vocab.strings[self.skip_token]
self.data[:] = self.skip_token
# Make array to store row numbers of documents that must be deleted
self.purged_docs = []
# This array will hold tokenized text data if it is asked for
if self.save_tokenized_text_data:
self.text_data = []
if self.tokenize_sents:
self.sentence_tokenize()
return
# If we want to merge phrases, we add s2v component
# to our pipe and it will do it for us.
if self.merge:
s2v = Sense2VecComponent('reddit_vectors-1.1.0')
self.nlp.add_pip(s2v)
for i, doc in enumerate(
self.nlp.pipe(self.texts,
n_threads=self.n_threads,
batch_size=10000)):
# noinspection PyBroadException
try:
# Create temp list for holding doc text
if self.save_tokenized_text_data:
doc_text = []
for token in doc:
# TODO - determine if you want to leave spaces or replace with underscores
# Replaces spaces between phrases with underscore
# text = token.text.replace(" ", "_")
# Get the string token for the given token type
if self.token_type == 'lower':
_token = token.lower_
elif self.token_type == 'lemma':
_token = token.lemma_
else:
_token = token.orth_
# Add token to spacy string list so we can use oov as known hash tokens
if token.is_oov:
self.nlp.vocab.strings.add(_token)
if self.save_tokenized_text_data:
doc_text.append(_token)
if self.save_tokenized_text_data:
self.text_data.append(doc_text)
# Options for how to tokenize
if self.token_type == 'lower':
dat = doc.to_array([
LOWER, LIKE_EMAIL, LIKE_URL, IS_OOV, IS_PUNCT, IS_ALPHA
])
elif self.token_type == 'lemma':
dat = doc.to_array([
LEMMA, LIKE_EMAIL, LIKE_URL, IS_OOV, IS_PUNCT, IS_ALPHA
])
else:
dat = doc.to_array([
ORTH, LIKE_EMAIL, LIKE_URL, IS_OOV, IS_PUNCT, IS_ALPHA
])
if len(dat) > 0:
assert dat.min(
) >= 0, 'Negative indices reserved for special tokens'
if self.skip_oov:
# Get indices of email, URL and oov tokens
idx = (dat[:, 1] > 0) | (dat[:, 2] > 0) | (dat[:, 3] >
0)
else:
# Get indices of email and URL tokens
idx = (dat[:, 1] > 0) | (dat[:, 2] > 0)
# Replace email and URL tokens with skip token
dat[idx] = self.skip_token
# Delete punctuation
if self.delete_punc:
delete = np.where(dat[:, 4] == 1)
dat = np.delete(dat, delete, 0)
if self.only_keep_alpha:
delete = np.where(dat[:, 5] == 0)
dat = np.delete(dat, delete, 0)
length = min(len(dat), self.max_len)
self.data[i, :length] = dat[:length, 0].ravel()
except Exception:
exc_type, exc_obj, exc_tb = sys.exc_info()
filename = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print('\n\n')
print(exc_type, filename, exc_tb.tb_lineno)
self.purged_docs.append(i)
continue
# If necessary, delete documents that failed to tokenize correctly.
self.data = np.delete(self.data, self.purged_docs, 0).astype(np.uint64)
# Unique tokens
self.uniques = np.unique(self.data)
# Saved Spacy Vocab
self.vocab = self.nlp.vocab
# Making an idx to word mapping for vocab
self.hash_to_word = {}
# Insert padding id into the hash
self.hash_to_word[self.skip_token] = '<SKIP>'
# If lemma, insert pronoun ID into the hash
if self.token_type == 'lemma':
self.hash_to_word[self.nlp.vocab.strings['-PRON-']] = '-PRON-'
for v in self.uniques:
if v != self.skip_token:
# noinspection PyPep8,PyBroadException
try:
if self.token_type == 'lower':
self.hash_to_word[v] = self.nlp.vocab[v].lower_
elif self.token_type == 'lemma':
self.hash_to_word[v] = self.nlp.vocab[v].lemma_
else:
self.hash_to_word[v] = self.nlp.vocab[v].orth_
except Exception:
pass
def tokenize(self):
# This is here in case we want to tokenize more documents later
if self.texts == None:
if self.context == False:
# Read in text data from textfile path
self.texts = open(self.textfile).read().split('\n')
self.texts = [str(t) for t in self.texts]
print("made texts")
else:
filename, file_extension = os.path.splitext(self.textfile)
if file_extension == ".json":
# Read in json data as dataframe
df = pd.read_json(self.textfile, lines=True)
else:
# Read in data as dataframe
df = pd.read_csv(self.textfile,
sep=self.sep,
usecols=self.usecols)
# Extract the text
text_col_name = self.usecols[0]
self.texts = df[text_col_name].values.astype(str).tolist()
# Small memory reduction by deleting this
del df[text_col_name]
self.context_df = df
# Get number of documents supplied
self.num_docs = len(self.texts)
# Init data as a bunch of zeros - shape [num_texts, max_length]
self.data = np.zeros((len(self.texts), self.max_length),
dtype=np.uint64)
if not self.tokenizing_new:
# Add the skip token to the vocab, creating a unique hash for it
self.nlp.vocab.strings.add(self.skip)
self.skip = self.nlp.vocab.strings[self.skip]
self.data[:] = self.skip
# Make array to store row numbers of documents that must be deleted
self.purged_docs = []
# This array will hold tokenized text data if it is asked for
if self.save_tokenized_text_data:
self.text_data = []
if self.tokenize_sentences:
self.sentence_tokenize()
return
# If we want to merge phrases, we add s2v component
# to our pipe and it will do it for us.
if self.merge:
s2v = Sense2VecComponent('reddit_vectors-1.1.0')
self.nlp.add_pipe(s2v)
for row, doc in enumerate(
self.nlp.pipe(self.texts,
n_threads=self.num_threads,
batch_size=10000)):
try:
# Create temp list for holding doc text
if self.save_tokenized_text_data:
doc_text = []
# Loop through tokens in doc
for token in doc:
# TODO - determine if you want to leave spaces or replace with underscores
# Replaces spaces between phrases with underscore
# text = token.text.replace(" ", "_")
# Get the string token for the given token type
if self.token_type == "lower":
_token = token.lower_
elif self.token_type == "lemma":
_token = token.lemma_
else:
_token = token.orth_
# Add token to spacy string list so we can use oov as known hash tokens
if token.is_oov:
self.nlp.vocab.strings.add(_token)
if self.save_tokenized_text_data:
doc_text.append(_token)
if self.save_tokenized_text_data:
self.text_data.append(doc_text)
# Options for how to tokenize
if self.token_type == "lower":
dat = doc.to_array([
LOWER, LIKE_EMAIL, LIKE_URL, IS_OOV, IS_PUNCT, IS_ALPHA
])
elif self.token_type == "lemma":
dat = doc.to_array([
LEMMA, LIKE_EMAIL, LIKE_URL, IS_OOV, IS_PUNCT, IS_ALPHA
])
else:
dat = doc.to_array([
ORTH, LIKE_EMAIL, LIKE_URL, IS_OOV, IS_PUNCT, IS_ALPHA
])
if len(dat) > 0:
msg = "Negative indices reserved for special tokens"
assert dat.min() >= 0, msg
if self.skip_oov:
# Get Indexes of email and URL and oov tokens
idx = (dat[:, 1] > 0) | (dat[:, 2] > 0) | (dat[:, 3] >
0)
else:
# Get Indexes of email and URL tokens
idx = (dat[:, 1] > 0) | (dat[:, 2] > 0)
# Replace email and URL tokens with skip token
dat[idx] = self.skip
# Delete punctuation
if self.delete_punctuation:
delete = np.where(dat[:, 4] == 1)
dat = np.delete(dat, delete, 0)
if self.only_keep_alpha == True:
delete = np.where(dat[:, 5] == 0)
dat = np.delete(dat, delete, 0)
length = min(len(dat), self.max_length)
self.data[row, :length] = dat[:length, 0].ravel()
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print("\n\n")
print(exc_type, fname, exc_tb.tb_lineno)
# print("Warning! Document", row, "broke, likely due to spaCy merge issues.\nMore info at their github, issues #1547 and #1474")
self.purged_docs.append(row)
continue
# If necessary, delete documents that failed to tokenize correctly.
self.data = np.delete(self.data, self.purged_docs, 0).astype(np.uint64)
# Unique tokens
self.uniques = np.unique(self.data)
# Saved Spacy Vocab
self.vocab = self.nlp.vocab
# Making an idx to word mapping for vocab
self.hash_to_word = {}
# Manually putting in this hash for the padding ID
self.hash_to_word[self.skip] = '<SKIP>'
# If lemma, manually put in hash for the pronoun ID
if self.token_type == "lemma":
self.hash_to_word[self.nlp.vocab.strings["-PRON-"]] = "-PRON-"
for v in self.uniques:
if v != self.skip:
try:
if self.token_type == "lower":
self.hash_to_word[v] = self.nlp.vocab[v].lower_
elif self.token_type == "lemma":
self.hash_to_word[v] = self.nlp.vocab[v].lemma_
else:
self.hash_to_word[v] = self.nlp.vocab[v].orth_
except:
pass
import nltk, spacy
from nltk.tokenize import sent_tokenize
from sense2vec import Sense2VecComponent
nltk.download('punkt')
# Resources for determining similarity: Spacy, sense2vec
s2v_path = "D:\\Programs\\Python37x64\\nlp_config\\s2v_reddit_2015_md"
spacy_lg_path = 'D:\\Programs\\Python37x64\\nlp_config\\venv\\Lib\\site-packages\\en_core_web_lg\\en_core_web_lg-2.2.5'
nlp = spacy.load(spacy_lg_path)
s2v = Sense2VecComponent(nlp.vocab).from_disk(s2v_path)
nlp.add_pipe(s2v)
seeds = {}
seeds['food']="food drink"
seeds['atms']= "atmosphere place environment"
seeds['serv']= "server management time"
seeds['prce']= "money expensive"
def avg(my_list):
if len(my_list) == 0:
return 0
return sum(my_list) / len(my_list)
def get_noun_toks(doc):
return [tok for tok in doc if tok.tag_.startswith('N')]
def calculate_similarity(noun, seed):
if not seed.has_vector or not noun.has_vector:
predargs = True
#use standard python randomizer
import random
#glob needed to find files
import glob
#nltk
import nltk
#sense2vec in combination with Spacy
import spacy
from sense2vec import Sense2VecComponent
spacynlp = spacy.load('en')
s2v = Sense2VecComponent('C:/Python27/ReqAnalyzing/reddit_vectors-1.1.0')
spacynlp.add_pipe(s2v)
#make deepcopy available
import copy
#grammar check
import grammar_check
tool = grammar_check.LanguageTool('en-GB')
'''
Construct requirements starts here
Proposed order.
1. Pick verb from allparts list
2. Check which frames/arguments are needed with verbnet
3. Find a semantically matching argument pair in allparts files
def __init__(self, model_name, sense2vec_path):
self.nlp = spacy.load(model_name)
s2v = Sense2VecComponent(self.nlp.vocab).from_disk(sense2vec_path)
self.nlp.add(s2v)
import spacy
from dataclasses import dataclass
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, root_validator
from sense2vec import Sense2VecComponent
from starlette.responses import Response
from starlette.status import HTTP_204_NO_CONTENT
app: FastAPI = FastAPI()
model: str = os.getenv('SPACY_MODEL')
pipeline_error: str = f"The model ({model}) doesn't support " + '{}.'
nlp: spacy = spacy.load(model)
if os.getenv('SENSE2VEC') == '1':
nlp.add_pipe(
Sense2VecComponent(nlp.vocab).from_disk('src/s2v_old')
)
def enforce_components(components: List[str], message: str) -> None:
"""Throws the <message> if the model doesn't have the <components>."""
for component in components:
if not nlp.has_pipe(component):
raise HTTPException(
status_code=400,
detail=pipeline_error.format(message)
)
class NERRequest(BaseModel):
sections: List[str]
from pipelines import pipeline
from text2text.text_generator import TextGenerator
import nltk
from nltk.stem.porter import *
import spacy
from sense2vec import Sense2VecComponent
spacy_nlp = spacy.load("en_core_web_sm")
s2v = Sense2VecComponent(spacy_nlp.vocab).from_disk("./s2v_old")
spacy_nlp.add_pipe(s2v)
t5_generator = pipeline("question-generation")
t2t_generator = TextGenerator(output_type="question")
def generate_from_T5(context, n=5):
res = t5_generator(context)
ans = []
que = []
for i, r in enumerate(res):
if i < n:
ans.append(r['answer'])
que.append(r['question'])
return que, ans
def generate_from_t2t(context, n=5):
res = t2t_generator.predict([context] * n)
ans = []
que = []
for r in res: