tdiggelm

from types import UnionType
from typing import Annotated, Any, ClassVar, TypeVar, Union, get_args, get_origin

from pydantic import Discriminator, Field, RootModel

T = TypeVar("T")

__all__ = ["UnionRootModel"]

tdiggelm

from logging import getLogger
from functools import wraps, lru_cache
from typing import Callable, Any
from datetime import datetime

log = getLogger(__name__)


def ttl_cache(

tdiggelm

tdiggelm

import re
import nltk

class Analyzer:
    ''' This analyzer applies the following transformation to an input text:
    1. tokenize text into words
    2. lowercase transform words
    3. stem words
    4. filter stopwords
    5. replace all numbers with _num_

tdiggelm

! pip3 install nltk --user

import os
import tensorflow as tf
import numpy as np
print(tf.__version__)

import nltk
nltk.download('movie_reviews')
nltk.download('punkt')

tdiggelm

# coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# 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
#
# Unless required by applicable law or agreed to in writing, software

tdiggelm

import zipfile
from io import TextIOWrapper
import numpy as np
import tensorflow as tf
import os

GLOVE_EMBEDDINGS = {
    'glove.6B.50d'   : ('http://nlp.stanford.edu/data/glove.6B.zip'        , 50),
    'glove.6B.100d'  : ('http://nlp.stanford.edu/data/glove.6B.zip'        , 100),
    'glove.6B.200d'  : ('http://nlp.stanford.edu/data/glove.6B.zip'        , 200),

tdiggelm

import tensorflow as tf

class MultiHeadAttention(tf.keras.layers.Layer):
    """ Mirrors the implementation from paper 'Attention Is All You Need' and
        corresponding source code in
        https://github.com/google-research/bert/blob/master/modeling.py. """
    def __init__(self,
                 size_per_head=16,
                 num_attention_heads=12,
                 dropout_rate=0,

tdiggelm

import numpy as np
import matplotlib.pyplot as plt
def catplot(x, y, c, labels=None, title=None, n_categories=None, 
            s=10, alpha=0.65, cmap='rainbow', fig=None, ax=None,
            border={'color': '0.7', 'linewidth': 1}, facecolor='white'):
  if not fig:
    fig, ax = plt.subplots(1, 1)
  try:
    N = int(n_categories)
  except:

tdiggelm

Find: \\url{([^}]+)}
Replace: <$1>