Norod

import jax
from transformers import FlaxGPTNeoForCausalLM, AutoTokenizer

#model_name = 'Norod78/hebrew-gpt_neo-tiny'
#model_name = 'Norod78/hebrew_poetry-gpt_neo-small'
model_name = 'Norod78/hebrew-gpt_neo-small'

model = FlaxGPTNeoForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Norod

import gradio as gr
from transformers import pipeline

title = "Hebrew GPT-Neo Demo"
description = ""
article = "<p></p>"
examples = [
    ['צחוקים ושיגועים'],
    ["למנשה פומפרניקל יש"],
    ["פעם אחת לפני שנים רבות"]

Norod

# !pip install sentencepiece transformers tokenizers

from transformers import MarianTokenizer, MarianMTModel
from typing import List

src = "en"  # source language

trg = "he"  # target language

model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"

Norod

# crapify_images.py
# Go over a folder of images and reduce the quality and/or add noise to some of them (Useful for making a Pix2Pix model more resilient) 
# @Norod78

import skimage
import skimage.io
import skimage.io._plugins.pil_plugin as pp
import numpy as np

from PIL import Image

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

Norod

Norod

Norod

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

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]: