Norod

"""
Recreate the Core ML model from scratch using
coremltools' neural_network.NeuralNetworkBuilder
"""
import coremltools
import coremltools.models.datatypes as datatypes
from coremltools.models import neural_network as neural_network
from coremltools.models.utils import save_spec
import numpy as np

Norod

#!/usr/bin/env python

# Copyright 2016 Google, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#

Norod

from tika import parser # pip install tika

import os

FOLDER_WITH_PDF="./"
files = [f for f in os.listdir(FOLDER_WITH_PDF) if f.endswith('.pdf')]

for infile in files: 
   full_path = os.path.join(FOLDER_WITH_PDF, infile)
   raw = parser.from_file(full_path)

Norod

# Add a "QA" top and bottom bars to App Icons
# Using ImageMagick
#brew install imagemagick

########
# Phone
########

convert "App Icon 1024x1024.png" \
    -size 1024x128 -font "Times New Roman"  -pointsize 96 -background 'rgb(106, 228, 222)'  -fill red \

Norod

import onnx

onnx_model = onnx.load('./input.onnx')

#Rename 'inp' to 'inst'
endpoint_names = ['inp', 'inst']

for i in range(len(onnx_model.graph.node)):
	for j in range(len(onnx_model.graph.node[i].input)):
		if onnx_model.graph.node[i].input[j] == endpoint_names[0]:

Norod

from transformers import AutoTokenizer, AutoModelForCausalLM
  
#pip install tokenizers==0.10.3 transformers==4.8.0

tokenizer = AutoTokenizer.from_pretrained("Norod78/distilgpt2-base-pretrained-he")
model = AutoModelForCausalLM.from_pretrained("Norod78/distilgpt2-base-pretrained-he", pad_token_id=tokenizer.eos_token_id)

prompt_text = "הנבחרת האולימפית של ישראל זכתה השנה"
max_len = 50

Norod

Norod

Norod

Norod

# !pip install sentencepiece transformers tokenizers

from transformers import MarianTokenizer, MarianMTModel
from typing import List
import csv

src = "en"  # source language

trg = "he"  # target language