prakhar21

import os
import time
import random
import re
from slackclient import SlackClient
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions

prakhar21

y = readability_scores
N = len(y)
x = [i+1 for i in range(N)]
width = 1/1.5

mini = min(readability_scores)
maxi = max(readability_scores)

pylab.title("Readability Comparison of text")
pylab.xlabel("Book")

prakhar21

import sys
import time
import glob
import codecs
import matplotlib.pyplot as plt
import requests

# Books present
books = sorted(glob.glob("data/harrypotter/*.txt"))

prakhar21

from happytransformer import GENSettings

top_k_sampling_settings = GENSettings(do_sample=True, top_k=50, max_length=30, min_length=10)
output_top_k_sampling = gpt_neo.generate_text("Iphone ", args=top_k_sampling_settings)

print (output_top_k_sampling.text)
# iphones are very good for keeping track of calls

prakhar21

from happytransformer import HappyGeneration, GENTrainArgs

gpt_neo = HappyGeneration("GPT-Neo", "EleutherAI/gpt-neo-125M") 

train_args = GENTrainArgs(num_train_epochs=1, learning_rate=2e-05, batch_size=2) 

gpt_neo.train("train.txt", args=train_args)

prakhar21

from happytransformer import HappyGeneration
gpt_neo = HappyGeneration(model_type="GPT-NEO", model_name="EleutherAI/gpt-neo-125M") 

print (gpt_neo)

prakhar21

from transformers import AutoModelForQuestionAnswering, AutoTokenizer
from transformers_interpret import QuestionAnsweringExplainer

tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
model = AutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")

qa_explainer = QuestionAnsweringExplainer(
    model,
    tokenizer,
)

prakhar21

from transformers_interpret import SequenceClassificationExplainer

sample_text = """A very classy nice restaurant. A warm welcoming, followed by an excellent service, with a lot of attention to details on order to please you."""

multiclass_explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer)
word_attributions = multiclass_explainer(text=sample_text)

print (word_attributions)

prakhar21

from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("sampathkethineedi/industry-classification")
model = AutoModelForSequenceClassification.from_pretrained(
    "sampathkethineedi/industry-classification"
)

print (tokenizer, model)

prakhar21

"""
@author: Prakhar Mishra
"""

import streamlit as st
from easynmt import EasyNMT
model = EasyNMT('opus-mt')


LANGS = [('en', 'es'), ('en', 'fr'), ('en', 'de'), ('en', 'ar'), ('en', 'tr'), ('en', 'ko')]