hadifar

Data Visualization

A chart is read by people and executed by you. This skill turns "make it look good" into a procedure with checks, so the result is right by construction rather than by taste.

The method here is design-system-agnostic. Nothing in the procedure, the form heuristic, the six checks, or the mark specs is specific to one product. A design system supplies a small set of parameters (its ramps, a categorical order, a diverging pair, a status palette, a texture, its surfaces, its filter components);

hadifar

{
    // ════════════════════════════════════════════════════════════
    //  EDITOR — applies everywhere, then refined in the [python] scope
    // ════════════════════════════════════════════════════════════
    "editor.formatOnSave": true,
    "editor.formatOnPaste": false, // paste-format is noisy; save-format is enough
    "editor.rulers": [
        88
    ], // visual guide at Ruff/Black default line length
    "editor.tabSize": 4,

hadifar

from transformers import pipeline

classifier = pipeline("text-classification", model="hadifar/xxx")


try:
    while True:
        user_input = input("Enter a string (Press Ctrl+C to stop): ")
        if user_input:
            res = classifier([user_input])

hadifar

model_name = 'model/classifier'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

model.push_to_hub('hadifar/xxx')
tokenizer.push_to_hub('hadifar/xxx')

hadifar

import evaluate
import numpy as np
import random

import torch
from datasets import load_dataset
from transformers import AutoTokenizer
from transformers import DataCollatorWithPadding
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer

hadifar

import argparse
import os

import numpy as np
from joblib import dump, load
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC

hadifar

import re

import nltk


def get_verb_phrases(t):
    verb_phrases = []
    num_children = len(t)
    num_VP = sum(1 if t[i].label() == "VP" else 0 for i in range(0, num_children))

hadifar

import torch
from transformers import *

import sys, logging

print('cuda available? ', torch.cuda.is_available())
print('how many gpus?', torch.cuda.device_count())

logging.root.handlers = []
logging.basicConfig(level="INFO", format='%(asctime)s:%(levelname)s: %(message)s', stream=sys.stdout)

hadifar

import tensorflow as tf

vocabulary_size = 10000
embedding_size = 64
rnn_size = 64
batch_size = 512

# download dataset
(train_data, train_labels), (test_data, test_labels) = tf.keras.datasets.imdb.load_data(num_words=vocabulary_size)

hadifar

NUM_GPUS = 4
strategy = tf.contrib.distribute.MirroredStrategy(num_gpus=NUM_GPUS)
config = tf.estimator.RunConfig(train_distribute=strategy)
estimator = tf.estimator.Estimator(model, config=config)