codesnips

nlp

python 
from gensim.models import Word2Vec

sentences = [
    ['customer', 'refund', 'payment', 'issue'],
    ['login', 'authentication', 'password', 'reset'],
    ['delivery', 'shipment', 'tracking', 'delay'],

Word embeddings with gensim for semantic similarity tasks

word-embeddings gensim nlp
by Dr. Elena Vasquez 1 tab
python 
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model_name = 'distilbert-base-uncased-finetuned-sst-2-english'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

Using Hugging Face transformers for modern NLP inference

hugging-face transformers nlp
by Dr. Elena Vasquez 1 tab
python 
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import classification_report

pipeline = Pipeline([

Text vectorization with TF-IDF for strong classical baselines

tf-idf nlp text-classification
by Dr. Elena Vasquez 1 tab
python 
import spacy
from spacy.matcher import Matcher

nlp = spacy.load('en_core_web_sm')
matcher = Matcher(nlp.vocab)
matcher.add('INCIDENT_ID', [[{'TEXT': {'REGEX': '^INC-[0-9]{6}$'}}]])

Natural language processing with spaCy pipelines and custom rules

spacy nlp entity-extraction
by Dr. Elena Vasquez 1 tab