yashvijay

MAX_LEN = 100 # max is 512 for BERT

class text_dataset(Dataset):
    def __init__(self, X, y):
        
        self.X = X
        self.y = y
        
    def __getitem__(self,index):
        

yashvijay

from pytorch_transformers import BertConfig
from pytorch_transformers import BertModel

config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
        
class BertForSequenceClassification(nn.Module):
    def __init__(self, num_labels=2):
        super(BertForSequenceClassification, self).__init__()
        self.num_labels = num_labels

yashvijay

from pytorch_transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_or_path='bert-base-uncased')
input_text = "this is a piece of feedback"
tokenized = tokenizer.tokenize(input_text)
tokenizer.convert_tokens_to_ids(tokenized_text)

yashvijay

from setuptools import setup

setup(
     name="multiTag_custom_package",
     version="0.1",
     include_package_data=True,
     scripts=["utils/preprocess.py", "multiTag.py"]
)

yashvijay

class Predictor(object):
    """Interface for constructing custom predictors."""

    def predict(self, instances, **kwargs):
        """Performs custom prediction.

        Instances are the decoded values from the request. They have already
        been deserialized from JSON.

        Args:

yashvijay

import pickle
import numpy as np

class CustomPredictor(object):

    columns = ['UX_UI', 'Documentation', 'Performance', 'Bugs', 'Feature_Request',
       'Price', 'Customer_Support', 'Onboarding', 'Reporting',
       'Alerts_Notification', 'Value_Prop']

    def __init__(self, models):

yashvijay

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.ensemble import RandomForestClassifier

import preprocess

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder

import pandas as pd

yashvijay

import nltk
nltk.download('stopwords', download_dir='/tmp')
nltk.download('punkt', download_dir='/tmp')
nltk.download('averaged_perceptron_tagger', download_dir='/tmp')
nltk.download('wordnet', download_dir='/tmp')
nltk.data.path.append('/tmp')

import re
import string

yashvijay

["Alerts_Notification", "Bugs", "Customer_Support", "Documentation", "Feature_Request", "Onboarding", "Performance", "Price", "Reporting", "UX_UI", "Value_Prop"]

yashvijay

SHORTCUTS

Key/Command	Description
Tab	Auto-complete files and folder names
Ctrl + A	Go to the beginning of the line you are currently typing on
Ctrl + E	Go to the end of the line you are currently typing on
Ctrl + U	Clear the line before the cursor
Ctrl + K	Clear the line after the cursor
Ctrl + W	Delete the word before the cursor
Ctrl + T	Swap the last two characters before the cursor