From b4efbefa47672174394a8b6a27d4e7bc193bc224 Mon Sep 17 00:00:00 2001 From: bentrevett Date: Thu, 15 Jul 2021 22:41:50 +0100 Subject: [PATCH] started writing nbow model text --- 1_nbow.ipynb | 2029 +++++++++++++++++------------------------ assets/nbow_model.png | Bin 0 -> 33651 bytes assets/nbow_model.xml | 1 + 3 files changed, 822 insertions(+), 1208 deletions(-) create mode 100644 assets/nbow_model.png create mode 100644 assets/nbow_model.xml diff --git a/1_nbow.ipynb b/1_nbow.ipynb index e1de518..92f257f 100644 --- a/1_nbow.ipynb +++ b/1_nbow.ipynb @@ -1,5 +1,44 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "a36e41e8", + "metadata": {}, + "source": [ + "# 1 - NBoW\n", + "\n", + "In this series we'll be building a machine learning model to perform sentiment analysis -- a subset of text classification where the task is to detect if a given sentence is positive or negative -- using [PyTorch](https://github.com/pytorch/pytorch) and [torchtext](https://github.com/pytorch/text). The dataset used will be movie reviews from the [IMDb dataset](http://ai.stanford.edu/~amaas/data/sentiment/), which we'll obtain using the [datasets](https://github.com/huggingface/datasets) library.\n", + "\n", + "## Introduction\n", + "\n", + "In this first notebook, we'll start very simple with one of the most basic models for *NLP* (natural language processing): a *NBoW* (*neural bag-of-words*) model (also known as *continuous bag-of-words*, *CBoW*). The NBoW model are a strong, commonly used, baseline model for NLP tasks. They should be one of the first models you implement when performing sentiment analysis/text classification.\n", + "\n", + "![](assets/nbow_model.png)\n", + "\n", + "An NBoW model takes in a sequence of $T$ *tokens*, $X=\\{x_1,...,x_T\\} \\in \\mathbb{Z}^T$ and passes each token through an *embedding layer* to obtain a sequence of *embedding vectors*. The sequence of embedding vectors is just known as an *embedding*, $E=\\{e_1,...,e_T\\} \\in \\mathbb{R}^{T \\times D}$, where $D$ is known as the *embedding dimension*. It then *pools* the embeddings across the sequence dimension to get $P \\in \\mathbb{R}^D$ and then finally passes $P$ through a linear layer (also known as a fully connected layer), to get a prediction, $\\hat{Y} \\in \\mathbb{R}^C$, where $C$ is the number of classes. We'll explain what a token is, and what each of the layers -- embedding layer, pooling, and linear layer -- do in due course. \n", + "\n", + "A note on notation, what does something like $E=\\{e_1,...,e_T\\} \\in \\mathbb{R}^{T \\times D}$ mean? $\\mathbb{R}^{T \\times D}$ means a $T \\times D$ sized tensor full of real numbers, i.e. a `torch.FloatTensor`. $X=\\{x_1,...,x_T\\} \\in \\mathbb{Z}^T$ is a $T$ sized tensor full of integers, i.e. a `torch.LongTensor`.\n", + "\n", + "## Preparing Data\n", + "\n", + "Before we can implement our NBoW model, we first have to perform quite a few steps to get our data ready to use. NLP usually requires quite a lot of data wrangling beforehand, though libraries such as `datasets` and `torchtext` handle most of this for us.\n", + "\n", + "The steps to take are:\n", + "- importing modules\n", + "- loading data\n", + "- tokenizing data\n", + "- creating data splits\n", + "- creating a vocabulary\n", + "- numericalizing data\n", + "- creating the dataloaders\n", + "\n", + "### Importing Modules\n", + "\n", + "First, we'll import the required modules. \n", + "\n", + "We use the `datasets` module for handling datasets, `matplotlib` for plotting our results, `numpy` for numerical analysis, `torch` for tensor computations, `torch.nn` for neural networks, `torch.optim` for neural network optimizers, `torchtext` for text processing, and `tqdm` for process bars." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -21,31 +60,46 @@ ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "fcc98ce9", + "cell_type": "markdown", + "id": "a5478fc3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "seed = 0\n", + "We'll also make sure to set the random seeds for `torch` and `numpy`. This is to ensure this notebook is reproducable, i.e. we get the same results each time we run it.\n", "\n", - "torch.manual_seed(seed)" + "It is usually good practice to run your experiments multiple times with different random seeds -- both to measure the variance of your model and also to avoid having results only calculated with either \"good\" or \"bad\" seeds, i.e. being very lucky or unlucky with the randomness in the training process." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, + "id": "fcc98ce9", + "metadata": {}, + "outputs": [], + "source": [ + "seed = 0\n", + "\n", + "torch.manual_seed(seed)\n", + "np.random.seed(seed)" + ] + }, + { + "cell_type": "markdown", + "id": "55b1eb74", + "metadata": {}, + "source": [ + "Next, we'll load our dataset using the `datasets` library. The first argument is the name of the dataset and the `split` argument chooses which *splits* of the data we want. \n", + "\n", + "Datasets usually come in two or more *splits*, non-overlapping examples from the data, most commonly a *train split* -- which we train our model on -- and a *test split* -- which we evaluate our trained model on. There's also a *validation split*, which we'll talk more about later. The train, test and validation split are also commonly called the train, test and validation sets -- we'll use split and set interchangeably\n", + " in these tutorials -- and the dataset usually refers to all three of the sets combined. The IMDb dataset actually comes with a third split, called *unsupervised*, which contains a bunch of examples without labels. We don't want these so we don't include them in our `split` argument. Note that if we didn't pass an argument to `split` then it would load all available splits of the data.\n", + "\n", + "How do we know that we have to use \"imdb\" for the IMDb dataset and that there's an \"unsupervised\" split? The `datasets` library has a great website used to browse the available datasets, see: https://huggingface.co/datasets/. By navigating to the [IMDb dataset page](https://huggingface.co/datasets/imdb) we can see more information specifically about the IMDb dataset.\n", + "\n", + "The output received when loading the dataset tells us that it is using a locally cached version instead of downloading the dataset from online." + ] + }, + { + "cell_type": "code", + "execution_count": 63, "id": "798f5387", "metadata": {}, "outputs": [ @@ -58,12 +112,20 @@ } ], "source": [ - "train_data, test_data = datasets.load_dataset('imdb', split=['train', 'test'])" + "train_data, test_data = datasets.load_dataset(\"imdb\", split=[\"train\", \"test\"])" + ] + }, + { + "cell_type": "markdown", + "id": "93721296", + "metadata": {}, + "source": [ + "We can print out the splits which shows us the *features* and *num_rows* of the dataset. num_rows are the number of examples in split, as we can see, there are 25,000 examples in each. Each example in a dataset provided by the `datasets` library is a dictionary, and the features are the keys which appear in every one of those dictionaries/examples. So, each example in the IMDb dataset has a *text* and a *label* key." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 64, "id": "42338609", "metadata": {}, "outputs": [ @@ -80,7 +142,7 @@ " }))" ] }, - "execution_count": 4, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -89,9 +151,47 @@ "train_data, test_data" ] }, + { + "cell_type": "markdown", + "id": "8ec70556", + "metadata": {}, + "source": [ + "We can check the `features` attribute of a split to get more information about the features. We can see that *text* is a `Value` of `dtype=string` -- in other words, it's a string -- and that *label* is a `ClassLabel`. A `ClassLabel` means the feature is an integer representation of which class the example belongs to. `num_classes=2` means that our labels are one of two values, 0 or 1, and `names=['neg', 'pos']` gives us the human-readable versions of those values. Thus, a label of 0 means the example is a negative review and a label of 1 means the example is a positive review." + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 65, + "id": "58f5cc56", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None),\n", + " 'text': Value(dtype='string', id=None)}" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.features" + ] + }, + { + "cell_type": "markdown", + "id": "84271369", + "metadata": {}, + "source": [ + "We can look at an example by indexing into the train set. As we can see, the text is quite noisy and also rambles on quite a bit." + ] + }, + { + "cell_type": "code", + "execution_count": 66, "id": "25a6e8cb", "metadata": {}, "outputs": [ @@ -102,7 +202,7 @@ " 'text': 'Bromwell High is a cartoon comedy. It ran at the same time as some other programs about school life, such as \"Teachers\". My 35 years in the teaching profession lead me to believe that Bromwell High\\'s satire is much closer to reality than is \"Teachers\". The scramble to survive financially, the insightful students who can see right through their pathetic teachers\\' pomp, the pettiness of the whole situation, all remind me of the schools I knew and their students. When I saw the episode in which a student repeatedly tried to burn down the school, I immediately recalled ......... at .......... High. A classic line: INSPECTOR: I\\'m here to sack one of your teachers. STUDENT: Welcome to Bromwell High. I expect that many adults of my age think that Bromwell High is far fetched. What a pity that it isn\\'t!'}" ] }, - "execution_count": 5, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -112,30 +212,113 @@ ] }, { - "cell_type": "code", - "execution_count": 6, - "id": "3017c0ab", + "cell_type": "markdown", + "id": "f8536207", "metadata": {}, - "outputs": [], "source": [ - "tokenizer = torchtext.data.utils.get_tokenizer('basic_english')" + "One of the first things we need to do to our data is *tokenize* it. Machine learning models aren't designed to handle strings, they're design to handle numbers. So what we need to do is break down our string into individual *tokens*, and then convert these tokens to numbers. We'll get to the conversion later, but first we'll look at *tokenization*.\n", + "\n", + "Tokenization involves using a *tokenizer* to process the strings in our dataset. A tokenizer is a function that goes from a string to a list of strings. There are many types of tokenizers available, but we're going to use a relatively simple one provided by `torchtext` called the `basic_english` tokenizer. We load our tokenizer as such:" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 67, + "id": "3017c0ab", + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = torchtext.data.utils.get_tokenizer(\"basic_english\")" + ] + }, + { + "cell_type": "markdown", + "id": "4db58859", + "metadata": {}, + "source": [ + "We can use the tokenizer by calling it on a string.\n", + "\n", + "Notice it creates a token by splitting the word on spaces, puts punctuation as its own token, and also lowercases every single word.\n", + "\n", + "The `get_tokenizer` function also supports other tokenizers, such as ones provided by [spaCy](https://spacy.io/) and [nltk](https://www.nltk.org/). " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "2d0de969", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['hello',\n", + " 'world',\n", + " '!',\n", + " 'how',\n", + " 'are',\n", + " 'you',\n", + " 'doing',\n", + " 'today',\n", + " '?',\n", + " 'i',\n", + " \"'\",\n", + " 'm',\n", + " 'doing',\n", + " 'fantastic',\n", + " '!']" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer(\"Hello world! How are you doing today? I'm doing fantastic!\")" + ] + }, + { + "cell_type": "markdown", + "id": "593711b9", + "metadata": {}, + "source": [ + "Now we have our tokenizer defined, we want to actually tokenize our data.\n", + "\n", + "Each dataset provided by the `datasets` library is an instance of a `Dataset` class. We can see all the methods in a `Dataset` [here](https://huggingface.co/docs/datasets/package_reference/main_classes.html#dataset), but the main one we are interested in is [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map). By using `map` we can apply a function to every example in the dataset and either update the example or create a new feature.\n", + "\n", + "We define the `tokenize_example` function below which takes in an `example`, a `tokenizer` and a `max_length` argument, tokenizes the text in the example, given by `example['text']`, trims the tokens to a maximum length and then returns a dictionary with the new feature name and feature value for that example. Note that the first argument to a function which we are going to `map` must always be the example dictionary, and it must always return a dictionary where the keys are the feature names and the values are the feature values to be added to this example. \n", + "\n", + "We're trimming the tokens to a maximum length here as some examples are unnecessarily long and we can predict sentiment pretty well just using the first couple of hundred tokens -- though this might not be true for you if you're using a different dataset!" + ] + }, + { + "cell_type": "code", + "execution_count": 69, "id": "876ad3b9", "metadata": {}, "outputs": [], "source": [ - "def tokenize_data(example, tokenizer, max_length):\n", + "def tokenize_example(example, tokenizer, max_length):\n", " tokens = tokenizer(example['text'])[:max_length]\n", " return {'tokens': tokens}" ] }, + { + "cell_type": "markdown", + "id": "35129a1b", + "metadata": {}, + "source": [ + "We apply the `tokenize_example` function below, on both the train and test sets. Any arguments to the function -- that aren't the example -- need to be passed as the `fn_kwargs` dictionary, with the keys being the argument names and the values the value passed to that argument.\n", + "\n", + "Operations on a `Dataset` are **not** performed in-place. You should always return the result into a new variable.\n", + "\n", + "Note the warnings showing that as I have performed this `map` before, the results are cached and are thus loaded from the cache instead of being calculated again." + ] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 70, "id": "5e295030", "metadata": {}, "outputs": [ @@ -151,13 +334,21 @@ "source": [ "max_length = 256\n", "\n", - "train_data = train_data.map(tokenize_data, fn_kwargs={'tokenizer': tokenizer, 'max_length': max_length})\n", - "test_data = test_data.map(tokenize_data, fn_kwargs={'tokenizer': tokenizer, 'max_length': max_length})" + "train_data = train_data.map(tokenize_example, fn_kwargs={'tokenizer': tokenizer, 'max_length': max_length})\n", + "test_data = test_data.map(tokenize_example, fn_kwargs={'tokenizer': tokenizer, 'max_length': max_length})" + ] + }, + { + "cell_type": "markdown", + "id": "a61b38c0", + "metadata": {}, + "source": [ + "We can now see that our `train_data` has a *tokens* feature -- as \"tokens\" was a key in the dictionary returned by the function we used for the `map`." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 71, "id": "f647bdf9", "metadata": {}, "outputs": [ @@ -170,7 +361,7 @@ "})" ] }, - "execution_count": 9, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -179,209 +370,105 @@ "train_data" ] }, + { + "cell_type": "markdown", + "id": "db3443a0", + "metadata": {}, + "source": [ + "By looking at the `features` attribute we can see it has automatically added the information about the tokens feature -- each is a sequence (a list) of strings. A `length=-1` means that all of our token sequences are not the same length." + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 75, + "id": "1605d52b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None),\n", + " 'text': Value(dtype='string', id=None),\n", + " 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.features" + ] + }, + { + "cell_type": "markdown", + "id": "1735d91a", + "metadata": {}, + "source": [ + "We can check the first example in our train set to see the result of the tokenization:" + ] + }, + { + "cell_type": "code", + "execution_count": 74, "id": "2f3de3b9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'label': 1,\n", - " 'text': 'Bromwell High is a cartoon comedy. It ran at the same time as some other programs about school life, such as \"Teachers\". My 35 years in the teaching profession lead me to believe that Bromwell High\\'s satire is much closer to reality than is \"Teachers\". The scramble to survive financially, the insightful students who can see right through their pathetic teachers\\' pomp, the pettiness of the whole situation, all remind me of the schools I knew and their students. When I saw the episode in which a student repeatedly tried to burn down the school, I immediately recalled ......... at .......... High. A classic line: INSPECTOR: I\\'m here to sack one of your teachers. STUDENT: Welcome to Bromwell High. I expect that many adults of my age think that Bromwell High is far fetched. What a pity that it isn\\'t!',\n", - " 'tokens': ['bromwell',\n", - " 'high',\n", - " 'is',\n", - " 'a',\n", - " 'cartoon',\n", - " 'comedy',\n", - " '.',\n", - " 'it',\n", - " 'ran',\n", - " 'at',\n", - " 'the',\n", - " 'same',\n", - " 'time',\n", - " 'as',\n", - " 'some',\n", - " 'other',\n", - " 'programs',\n", - " 'about',\n", - " 'school',\n", - " 'life',\n", - " ',',\n", - " 'such',\n", - " 'as',\n", - " 'teachers',\n", - " '.',\n", - " 'my',\n", - " '35',\n", - " 'years',\n", - " 'in',\n", - " 'the',\n", - " 'teaching',\n", - " 'profession',\n", - " 'lead',\n", - " 'me',\n", - " 'to',\n", - " 'believe',\n", - " 'that',\n", - " 'bromwell',\n", - " 'high',\n", - " \"'\",\n", - " 's',\n", - " 'satire',\n", - " 'is',\n", - " 'much',\n", - " 'closer',\n", - " 'to',\n", - " 'reality',\n", - " 'than',\n", - " 'is',\n", - " 'teachers',\n", - " '.',\n", - " 'the',\n", - " 'scramble',\n", - " 'to',\n", - " 'survive',\n", - " 'financially',\n", - " ',',\n", - " 'the',\n", - " 'insightful',\n", - " 'students',\n", - " 'who',\n", - " 'can',\n", - " 'see',\n", - " 'right',\n", - " 'through',\n", - " 'their',\n", - " 'pathetic',\n", - " 'teachers',\n", - " \"'\",\n", - " 'pomp',\n", - " ',',\n", - " 'the',\n", - " 'pettiness',\n", - " 'of',\n", - " 'the',\n", - " 'whole',\n", - " 'situation',\n", - " ',',\n", - " 'all',\n", - " 'remind',\n", - " 'me',\n", - " 'of',\n", - " 'the',\n", - " 'schools',\n", - " 'i',\n", - " 'knew',\n", - " 'and',\n", - " 'their',\n", - " 'students',\n", - " '.',\n", - " 'when',\n", - " 'i',\n", - " 'saw',\n", - " 'the',\n", - " 'episode',\n", - " 'in',\n", - " 'which',\n", - " 'a',\n", - " 'student',\n", - " 'repeatedly',\n", - " 'tried',\n", - " 'to',\n", - " 'burn',\n", - " 'down',\n", - " 'the',\n", - " 'school',\n", - " ',',\n", - " 'i',\n", - " 'immediately',\n", - " 'recalled',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " 'at',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " '.',\n", - " 'high',\n", - " '.',\n", - " 'a',\n", - " 'classic',\n", - " 'line',\n", - " 'inspector',\n", - " 'i',\n", - " \"'\",\n", - " 'm',\n", - " 'here',\n", - " 'to',\n", - " 'sack',\n", - " 'one',\n", - " 'of',\n", - " 'your',\n", - " 'teachers',\n", - " '.',\n", - " 'student',\n", - " 'welcome',\n", - " 'to',\n", - " 'bromwell',\n", - " 'high',\n", - " '.',\n", - " 'i',\n", - " 'expect',\n", - " 'that',\n", - " 'many',\n", - " 'adults',\n", - " 'of',\n", - " 'my',\n", - " 'age',\n", - " 'think',\n", - " 'that',\n", - " 'bromwell',\n", - " 'high',\n", - " 'is',\n", - " 'far',\n", - " 'fetched',\n", - " '.',\n", - " 'what',\n", - " 'a',\n", - " 'pity',\n", - " 'that',\n", - " 'it',\n", - " 'isn',\n", - " \"'\",\n", - " 't',\n", - " '!']}" + "['bromwell',\n", + " 'high',\n", + " 'is',\n", + " 'a',\n", + " 'cartoon',\n", + " 'comedy',\n", + " '.',\n", + " 'it',\n", + " 'ran',\n", + " 'at',\n", + " 'the',\n", + " 'same',\n", + " 'time',\n", + " 'as',\n", + " 'some',\n", + " 'other',\n", + " 'programs',\n", + " 'about',\n", + " 'school',\n", + " 'life',\n", + " ',',\n", + " 'such',\n", + " 'as',\n", + " 'teachers',\n", + " '.']" ] }, - "execution_count": 10, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "train_data[0]" + "train_data[0]['tokens'][:25]" + ] + }, + { + "cell_type": "markdown", + "id": "04d4ee14", + "metadata": {}, + "source": [ + "Next up, we'll create a *validation set* from our data. This is similar to our test set in that we do not train our model on it, we only evaluate our model on it. \n", + "\n", + "Why have both a validation set and a test set? Your test set respresents the real world data that you'd see if you actually deployed this model. You won't be able to see what data your model will be fed once deployed, and your test set is supposed to reflect that. Every time we tune our model hyperparameters or training set-up to make it do a bit better on the test set, we are leak information from the test set into the training process. If we do this too often then we begin to overfit on the test set. Hence, we need some data which can act as a \"proxy\" test set which we can look at more frequently in order to evaluate how well our model actually does on unseen data -- this is the validation set.\n", + "\n", + "We can split a `Dataset` using the `train_test_split` method which splits a dataset into two, creating a `DatasetDict` for each split, one called `train` and another called `test` -- a bit confusing because these are our train and validation sets, not the test. We use `test_size` to set the portion of the data used for the validation set -- 0.25 means we use 25% of the training set -- and the examples are chosen randomly." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "15e48bfb", "metadata": {}, "outputs": [ @@ -389,7 +476,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Loading cached split indices for dataset at /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a/cache-90b2a85f23273ecd.arrow and /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a/cache-99371bdf1a536e7c.arrow\n" + "Loading cached split indices for dataset at /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a/cache-09bdb9cf28fcbb3c.arrow and /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a/cache-8e0a9e291c417a75.arrow\n" ] } ], @@ -402,286 +489,16 @@ ] }, { - "cell_type": "code", - "execution_count": 12, - "id": "881e83b3", + "cell_type": "markdown", + "id": "870c829b", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'label': 1,\n", - " 'text': \"Made in 1946 and released in 1948, The Lady and Shanghai was one of the big films made by Welles after returning from relative exile for making Citizen Kane. Dark, brooding and expressing some early Cold War paranoia, this film stands tall as a Film-Noir crime film. The cinematography of this film is filled with Welles' characteristic quirks of odd angles, quick cuts, long pans and sinister lighting. The use of ambient street music is a precursor to the incredible long opening shot in Touch of Evil, and the mysterious Chinese characters and the sequences in Chinatown can only be considered as the inspiration, in many ways, to Roman Polanski's Chinatown. Unfortunately, it is Welles' obsession with technical filmmaking that hurts this film in its entirety. The plot of this story is often lost behind a sometimes incomprehensible clutter of film techniques.

However, despite this criticism, the story combined with wonderful performances by Welles, Hayworth and especially Glenn Anders (Laughter) make this film a joy to watch. Orson Welles pulls off not only the Irish brogue, but the torn identities as the honest but dangerous sailor. Rita Hayworth, who was married to Welles at the time, breaks with her usual roles as a sex goddess and takes on a role of real depth and contradictions. Finally, Glenn Anders strange and bizarre portrayal or Elsa's husbands' law partner is nothing short of classic!\",\n", - " 'tokens': ['made',\n", - " 'in',\n", - " '1946',\n", - " 'and',\n", - " 'released',\n", - " 'in',\n", - " '1948',\n", - " ',',\n", - " 'the',\n", - " 'lady',\n", - " 'and',\n", - " 'shanghai',\n", - " 'was',\n", - " 'one',\n", - " 'of',\n", - " 'the',\n", - " 'big',\n", - " 'films',\n", - " 'made',\n", - " 'by',\n", - " 'welles',\n", - " 'after',\n", - " 'returning',\n", - " 'from',\n", - " 'relative',\n", - " 'exile',\n", - " 'for',\n", - " 'making',\n", - " 'citizen',\n", - " 'kane',\n", - " '.',\n", - " 'dark',\n", - " ',',\n", - " 'brooding',\n", - " 'and',\n", - " 'expressing',\n", - " 'some',\n", - " 'early',\n", - " 'cold',\n", - " 'war',\n", - " 'paranoia',\n", - " ',',\n", - " 'this',\n", - " 'film',\n", - " 'stands',\n", - " 'tall',\n", - " 'as',\n", - " 'a',\n", - " 'film-noir',\n", - " 'crime',\n", - " 'film',\n", - " '.',\n", - " 'the',\n", - " 'cinematography',\n", - " 'of',\n", - " 'this',\n", - " 'film',\n", - " 'is',\n", - " 'filled',\n", - " 'with',\n", - " 'welles',\n", - " \"'\",\n", - " 'characteristic',\n", - " 'quirks',\n", - " 'of',\n", - " 'odd',\n", - " 'angles',\n", - " ',',\n", - " 'quick',\n", - " 'cuts',\n", - " ',',\n", - " 'long',\n", - " 'pans',\n", - " 'and',\n", - " 'sinister',\n", - " 'lighting',\n", - " '.',\n", - " 'the',\n", - " 'use',\n", - " 'of',\n", - " 'ambient',\n", - " 'street',\n", - " 'music',\n", - " 'is',\n", - " 'a',\n", - " 'precursor',\n", - " 'to',\n", - " 'the',\n", - " 'incredible',\n", - " 'long',\n", - " 'opening',\n", - " 'shot',\n", - " 'in',\n", - " 'touch',\n", - " 'of',\n", - " 'evil',\n", - " ',',\n", - " 'and',\n", - " 'the',\n", - " 'mysterious',\n", - " 'chinese',\n", - " 'characters',\n", - " 'and',\n", - " 'the',\n", - " 'sequences',\n", - " 'in',\n", - " 'chinatown',\n", - " 'can',\n", - " 'only',\n", - " 'be',\n", - " 'considered',\n", - " 'as',\n", - " 'the',\n", - " 'inspiration',\n", - " ',',\n", - " 'in',\n", - " 'many',\n", - " 'ways',\n", - " ',',\n", - " 'to',\n", - " 'roman',\n", - " 'polanski',\n", - " \"'\",\n", - " 's',\n", - " 'chinatown',\n", - " '.',\n", - " 'unfortunately',\n", - " ',',\n", - " 'it',\n", - " 'is',\n", - " 'welles',\n", - " \"'\",\n", - " 'obsession',\n", - " 'with',\n", - " 'technical',\n", - " 'filmmaking',\n", - " 'that',\n", - " 'hurts',\n", - " 'this',\n", - " 'film',\n", - " 'in',\n", - " 'its',\n", - " 'entirety',\n", - " '.',\n", - " 'the',\n", - " 'plot',\n", - " 'of',\n", - " 'this',\n", - " 'story',\n", - " 'is',\n", - " 'often',\n", - " 'lost',\n", - " 'behind',\n", - " 'a',\n", - " 'sometimes',\n", - " 'incomprehensible',\n", - " 'clutter',\n", - " 'of',\n", - " 'film',\n", - " 'techniques',\n", - " '.',\n", - " 'however',\n", - " ',',\n", - " 'despite',\n", - " 'this',\n", - " 'criticism',\n", - " ',',\n", - " 'the',\n", - " 'story',\n", - " 'combined',\n", - " 'with',\n", - " 'wonderful',\n", - " 'performances',\n", - " 'by',\n", - " 'welles',\n", - " ',',\n", - " 'hayworth',\n", - " 'and',\n", - " 'especially',\n", - " 'glenn',\n", - " 'anders',\n", - " '(',\n", - " 'laughter',\n", - " ')',\n", - " 'make',\n", - " 'this',\n", - " 'film',\n", - " 'a',\n", - " 'joy',\n", - " 'to',\n", - " 'watch',\n", - " '.',\n", - " 'orson',\n", - " 'welles',\n", - " 'pulls',\n", - " 'off',\n", - " 'not',\n", - " 'only',\n", - " 'the',\n", - " 'irish',\n", - " 'brogue',\n", - " ',',\n", - " 'but',\n", - " 'the',\n", - " 'torn',\n", - " 'identities',\n", - " 'as',\n", - " 'the',\n", - " 'honest',\n", - " 'but',\n", - " 'dangerous',\n", - " 'sailor',\n", - " '.',\n", - " 'rita',\n", - " 'hayworth',\n", - " ',',\n", - " 'who',\n", - " 'was',\n", - " 'married',\n", - " 'to',\n", - " 'welles',\n", - " 'at',\n", - " 'the',\n", - " 'time',\n", - " ',',\n", - " 'breaks',\n", - " 'with',\n", - " 'her',\n", - " 'usual',\n", - " 'roles',\n", - " 'as',\n", - " 'a',\n", - " 'sex',\n", - " 'goddess',\n", - " 'and',\n", - " 'takes',\n", - " 'on',\n", - " 'a',\n", - " 'role',\n", - " 'of',\n", - " 'real',\n", - " 'depth',\n", - " 'and',\n", - " 'contradictions',\n", - " '.',\n", - " 'finally',\n", - " ',',\n", - " 'glenn',\n", - " 'anders',\n", - " 'strange',\n", - " 'and',\n", - " 'bizarre',\n", - " 'portrayal',\n", - " 'or',\n", - " 'elsa',\n", - " \"'\"]}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "train_data[0]" + "By showing the lengths of each split within our dataset, we can see the 25,000 training examples have now been split into 18,750 training examples and 6,250 validation examples, with the original 25,000 test examples remaining untouched." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "c227e4fc", "metadata": {}, "outputs": [ @@ -691,7 +508,7 @@ "(18750, 6250, 25000)" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -702,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "4865e94a", "metadata": {}, "outputs": [], @@ -717,17 +534,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "123ceb33", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "21543" + "21526" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -738,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "d4ec89de", "metadata": {}, "outputs": [ @@ -748,7 +565,7 @@ "['', '', 'the', '.', ',', 'a', 'and', 'of', 'to', \"'\"]" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -759,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "29ac49c8", "metadata": {}, "outputs": [ @@ -769,7 +586,7 @@ "0" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -782,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "447020e1", "metadata": {}, "outputs": [ @@ -792,7 +609,7 @@ "1" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -805,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "201b5383", "metadata": {}, "outputs": [ @@ -815,7 +632,7 @@ "False" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -826,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "7a951ea0", "metadata": {}, "outputs": [], @@ -836,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "407fe05d", "metadata": {}, "outputs": [ @@ -846,7 +663,7 @@ "0" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -857,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "id": "76518d11", "metadata": {}, "outputs": [], @@ -869,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "id": "dacaeaef", "metadata": {}, "outputs": [ @@ -877,9 +694,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Loading cached processed dataset at /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a/cache-4fa96f7122a515e2.arrow\n", - "Loading cached processed dataset at /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a/cache-cabd43c688223ded.arrow\n", - "Loading cached processed dataset at /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a/cache-087b09fd94e05553.arrow\n" + "Loading cached processed dataset at /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a/cache-d266a0df023fa6e2.arrow\n", + "Loading cached processed dataset at /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a/cache-296fd4058bb43b50.arrow\n", + "Loading cached processed dataset at /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a/cache-6bc06b9661e3abbb.arrow\n" ] } ], @@ -891,530 +708,336 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "id": "08751c45", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'label': 1,\n", - " 'text': \"Made in 1946 and released in 1948, The Lady and Shanghai was one of the big films made by Welles after returning from relative exile for making Citizen Kane. Dark, brooding and expressing some early Cold War paranoia, this film stands tall as a Film-Noir crime film. The cinematography of this film is filled with Welles' characteristic quirks of odd angles, quick cuts, long pans and sinister lighting. The use of ambient street music is a precursor to the incredible long opening shot in Touch of Evil, and the mysterious Chinese characters and the sequences in Chinatown can only be considered as the inspiration, in many ways, to Roman Polanski's Chinatown. Unfortunately, it is Welles' obsession with technical filmmaking that hurts this film in its entirety. The plot of this story is often lost behind a sometimes incomprehensible clutter of film techniques.

However, despite this criticism, the story combined with wonderful performances by Welles, Hayworth and especially Glenn Anders (Laughter) make this film a joy to watch. Orson Welles pulls off not only the Irish brogue, but the torn identities as the honest but dangerous sailor. Rita Hayworth, who was married to Welles at the time, breaks with her usual roles as a sex goddess and takes on a role of real depth and contradictions. Finally, Glenn Anders strange and bizarre portrayal or Elsa's husbands' law partner is nothing short of classic!\",\n", - " 'tokens': ['made',\n", - " 'in',\n", - " '1946',\n", - " 'and',\n", - " 'released',\n", - " 'in',\n", - " '1948',\n", - " ',',\n", - " 'the',\n", - " 'lady',\n", - " 'and',\n", - " 'shanghai',\n", - " 'was',\n", - " 'one',\n", - " 'of',\n", - " 'the',\n", - " 'big',\n", - " 'films',\n", - " 'made',\n", - " 'by',\n", - " 'welles',\n", - " 'after',\n", - " 'returning',\n", - " 'from',\n", - " 'relative',\n", - " 'exile',\n", - " 'for',\n", - " 'making',\n", - " 'citizen',\n", - " 'kane',\n", - " '.',\n", - " 'dark',\n", - " ',',\n", - " 'brooding',\n", - " 'and',\n", - " 'expressing',\n", - " 'some',\n", - " 'early',\n", - " 'cold',\n", - " 'war',\n", - " 'paranoia',\n", - " ',',\n", - " 'this',\n", - " 'film',\n", - " 'stands',\n", - " 'tall',\n", - " 'as',\n", - " 'a',\n", - " 'film-noir',\n", - " 'crime',\n", - " 'film',\n", - " '.',\n", - " 'the',\n", - " 'cinematography',\n", - " 'of',\n", - " 'this',\n", - " 'film',\n", + "{'label': 0,\n", + " 'text': 'This documentary is at its best when it is simply showing the ayurvedic healers\\' offices and treatment preparation. There is no denying the grinding poverty in India and desperation of even their wealthier clients. However, as an argument for ayurvedic medicine in general, this film fails miserably. Although Indian clients mention having seen \"aleopathic\" doctors, those doctors are not interviewed, and we have to take the vague statements of their patients at face value-- \"the doctor said there was no cure,\" \"the doctor said it was cancer\" etc. Well, \"no cure\" doesn\\'t mean \"no treatment,\" and what type of cancer exactly does the patient have? The film is at its most feeble when showing ayurvedic practice in America. There it is reduced, apparently, to the stunning suggestion that having a high powered Wall Street job can make your stomach hurt.',\n", + " 'tokens': ['this',\n", + " 'documentary',\n", " 'is',\n", - " 'filled',\n", - " 'with',\n", - " 'welles',\n", - " \"'\",\n", - " 'characteristic',\n", - " 'quirks',\n", - " 'of',\n", - " 'odd',\n", - " 'angles',\n", - " ',',\n", - " 'quick',\n", - " 'cuts',\n", - " ',',\n", - " 'long',\n", - " 'pans',\n", - " 'and',\n", - " 'sinister',\n", - " 'lighting',\n", - " '.',\n", - " 'the',\n", - " 'use',\n", - " 'of',\n", - " 'ambient',\n", - " 'street',\n", - " 'music',\n", - " 'is',\n", - " 'a',\n", - " 'precursor',\n", - " 'to',\n", - " 'the',\n", - " 'incredible',\n", - " 'long',\n", - " 'opening',\n", - " 'shot',\n", - " 'in',\n", - " 'touch',\n", - " 'of',\n", - " 'evil',\n", - " ',',\n", - " 'and',\n", - " 'the',\n", - " 'mysterious',\n", - " 'chinese',\n", - " 'characters',\n", - " 'and',\n", - " 'the',\n", - " 'sequences',\n", - " 'in',\n", - " 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- " 6041,\n", - " 7843,\n", + " 157,\n", + " 5592,\n", + " 30,\n", + " 29,\n", + " 8351,\n", " 4,\n", - " 42,\n", - " 17,\n", - " 922,\n", + " 6,\n", + " 78,\n", + " 31,\n", " 8,\n", - " 2302,\n", - " 38,\n", + " 203,\n", " 2,\n", - " 65,\n", - " 4,\n", - " 2100,\n", - " 20,\n", - " 50,\n", - " 604,\n", - " 556,\n", - " 19,\n", - " 5,\n", - " 416,\n", - " 11476,\n", - " 6,\n", - " 310,\n", - " 27,\n", - " 5,\n", - " 221,\n", + " 3400,\n", + " 6614,\n", " 7,\n", - " 158,\n", - " 1248,\n", - " 6,\n", - " 16505,\n", - " 3,\n", + " 77,\n", + " 5229,\n", + " 37,\n", " 454,\n", + " 0,\n", + " 2,\n", + " 937,\n", + " 307,\n", + " 46,\n", + " 17,\n", + " 66,\n", + " 4845,\n", + " 4,\n", + " 2,\n", + " 937,\n", + " 307,\n", + " 11,\n", + " 17,\n", + " 5362,\n", + " 487,\n", + " 3,\n", + " 82,\n", + " 4,\n", + " 66,\n", + " 4845,\n", + " 173,\n", + " 9,\n", + " 28,\n", + " 384,\n", + " 66,\n", + " 2407,\n", " 4,\n", - " 3111,\n", - " 14039,\n", - " 637,\n", " 6,\n", - " 1079,\n", - " 1074,\n", - " 49,\n", - " 8928,\n", - " 9]}" + " 55,\n", + " 618,\n", + " 7,\n", + " 5362,\n", + " 615,\n", + " 135,\n", + " 2,\n", + " 3307,\n", + " 31,\n", + " 56,\n", + " 2,\n", + " 23,\n", + " 10,\n", + " 37,\n", + " 100,\n", + " 94,\n", + " 6702,\n", + " 60,\n", + " 834,\n", + " 0,\n", + " 4335,\n", + " 13,\n", + " 865,\n", + " 3,\n", + " 46,\n", + " 11,\n", + " 10,\n", + " 4647,\n", + " 4,\n", + " 694,\n", + " 4,\n", + " 8,\n", + " 2,\n", + " 1253,\n", + " 5657,\n", + " 15,\n", + " 266,\n", + " 5,\n", + " 325,\n", + " 10526,\n", + " 1698,\n", + " 874,\n", + " 279,\n", + " 59,\n", + " 105,\n", + " 133,\n", + " 3035,\n", + " 1559,\n", + " 3]}" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1425,7 +1048,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "id": "678d0397", "metadata": {}, "outputs": [], @@ -1445,43 +1068,33 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "id": "be56bf90", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'label': tensor(1),\n", - " 'ids': tensor([ 98, 13, 6329, 6, 559, 13, 6491, 4, 2, 763,\n", - " 6, 6300, 17, 34, 7, 2, 195, 116, 98, 40,\n", - " 2302, 102, 3497, 44, 3318, 15422, 21, 261, 3609, 3433,\n", - " 3, 474, 4, 6093, 6, 10888, 54, 396, 1198, 338,\n", - " 4479, 4, 14, 23, 1481, 3596, 19, 5, 13453, 850,\n", - " 23, 3, 2, 639, 7, 14, 23, 10, 1073, 20,\n", - " 2302, 9, 7180, 9372, 7, 1045, 2522, 4, 1706, 2115,\n", - " 4, 212, 8127, 6, 3179, 1485, 3, 2, 386, 7,\n", - " 13210, 860, 233, 10, 5, 12948, 8, 2, 984, 212,\n", - " 628, 346, 13, 1228, 7, 462, 4, 6, 2, 1236,\n", - " 1675, 114, 6, 2, 905, 13, 10802, 59, 71, 35,\n", - " 1132, 19, 2, 3009, 4, 13, 117, 771, 4, 8,\n", - " 3582, 3534, 9, 16, 10802, 3, 446, 4, 11, 10,\n", - " 2302, 9, 3013, 20, 1810, 6389, 15, 4846, 14, 23,\n", - " 13, 100, 6865, 3, 2, 113, 7, 14, 64, 10,\n", - " 406, 443, 527, 5, 525, 4470, 10812, 7, 23, 3324,\n", - " 3, 190, 4, 500, 14, 3049, 4, 2, 64, 2675,\n", - " 20, 356, 389, 40, 2302, 4, 7843, 6, 262, 3111,\n", - " 14039, 25, 2146, 24, 106, 14, 23, 5, 1777, 8,\n", - " 108, 3, 4281, 2302, 2890, 137, 29, 71, 2, 2386,\n", - " 0, 4, 22, 2, 3544, 7847, 19, 2, 1172, 22,\n", - " 1813, 7915, 3, 6041, 7843, 4, 42, 17, 922, 8,\n", - " 2302, 38, 2, 65, 4, 2100, 20, 50, 604, 556,\n", - " 19, 5, 416, 11476, 6, 310, 27, 5, 221, 7,\n", - " 158, 1248, 6, 16505, 3, 454, 4, 3111, 14039, 637,\n", - " 6, 1079, 1074, 49, 8928, 9])}" + "{'label': tensor(0),\n", + " 'ids': tensor([ 14, 627, 10, 37, 100, 125, 60, 11, 10, 361,\n", + " 834, 2, 0, 0, 9, 12187, 6, 2407, 9694, 3,\n", + " 46, 10, 66, 8861, 2, 16732, 3705, 13, 2360, 6,\n", + " 4374, 7, 69, 77, 0, 13332, 3, 190, 4, 19,\n", + " 41, 4597, 21, 0, 6574, 13, 822, 4, 14, 23,\n", + " 962, 3426, 3, 265, 1267, 13332, 798, 266, 111, 0,\n", + " 5592, 4, 157, 5592, 30, 29, 8351, 4, 6, 78,\n", + " 31, 8, 203, 2, 3400, 6614, 7, 77, 5229, 37,\n", + " 454, 0, 2, 937, 307, 46, 17, 66, 4845, 4,\n", + " 2, 937, 307, 11, 17, 5362, 487, 3, 82, 4,\n", + " 66, 4845, 173, 9, 28, 384, 66, 2407, 4, 6,\n", + " 55, 618, 7, 5362, 615, 135, 2, 3307, 31, 56,\n", + " 2, 23, 10, 37, 100, 94, 6702, 60, 834, 0,\n", + " 4335, 13, 865, 3, 46, 11, 10, 4647, 4, 694,\n", + " 4, 8, 2, 1253, 5657, 15, 266, 5, 325, 10526,\n", + " 1698, 874, 279, 59, 105, 133, 3035, 1559, 3])}" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1490,6 +1103,43 @@ "train_data[0]" ] }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d97786a1", + "metadata": {}, + "outputs": [], + "source": [ + "def collate(batch, pad_index):\n", + " batch_ids = [i['ids'] for i in batch]\n", + " batch_ids = nn.utils.rnn.pad_sequence(batch_ids, padding_value=pad_index, batch_first=True)\n", + " batch_label = [i['label'] for i in batch]\n", + " batch_label = torch.stack(batch_label)\n", + " batch = {'ids': batch_ids,\n", + " 'label': batch_label}\n", + " return batch" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d3098a96", + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 512\n", + "\n", + "collate = functools.partial(collate, pad_index=pad_index)\n", + "\n", + "train_dataloader = torch.utils.data.DataLoader(train_data, \n", + " batch_size=batch_size, \n", + " collate_fn=collate, \n", + " shuffle=True)\n", + "\n", + "valid_dataloader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size, collate_fn=collate)\n", + "test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, collate_fn=collate)" + ] + }, { "cell_type": "markdown", "id": "d6ba2ac8", @@ -1500,7 +1150,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "id": "081f04a6", "metadata": {}, "outputs": [], @@ -1524,7 +1174,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "id": "97897898", "metadata": {}, "outputs": [], @@ -1538,7 +1188,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 32, "id": "4acc5118", "metadata": {}, "outputs": [ @@ -1546,7 +1196,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The model has 6,463,502 trainable parameters\n" + "The model has 6,458,402 trainable parameters\n" ] } ], @@ -1559,7 +1209,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 33, "id": "866e0b64", "metadata": {}, "outputs": [], @@ -1569,7 +1219,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 34, "id": "ead7be53", "metadata": {}, "outputs": [], @@ -1579,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 35, "id": "1a64ead7", "metadata": {}, "outputs": [ @@ -1589,7 +1239,7 @@ "torch.Size([300])" ] }, - "execution_count": 32, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1600,7 +1250,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 36, "id": "7ecc5d88", "metadata": {}, "outputs": [ @@ -1669,7 +1319,7 @@ " -3.3574e-01, -3.3371e-01, 8.6787e-02, 2.4920e-01, 6.5367e-02])" ] }, - "execution_count": 33, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1680,7 +1330,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 37, "id": "e8540b4b", "metadata": {}, "outputs": [], @@ -1690,17 +1340,17 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 38, "id": "9d31228e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "torch.Size([21543, 300])" + "torch.Size([21526, 300])" ] }, - "execution_count": 35, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1711,7 +1361,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 39, "id": "3a6f4173", "metadata": {}, "outputs": [ @@ -1723,75 +1373,9 @@ " [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", " [ 0.1483, 2.4187, 1.3279, ..., -1.0328, 1.1305, -0.5703],\n", " ...,\n", - " [-0.9882, -0.5407, 1.2382, ..., 2.4935, 1.0714, -0.7917],\n", - " [-1.2230, 0.6308, 1.7523, ..., 0.9265, -0.1116, -0.3872],\n", - " [-1.6577, 0.1200, -0.0599, ..., -0.5380, 0.5277, -0.0379]],\n", - " requires_grad=True)" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.embedding.weight" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "5c1cbd5c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", - " [-0.0653, -0.0930, -0.0176, ..., 0.1664, -0.1308, 0.0354],\n", - " ...,\n", - " [-0.1329, 0.2494, -0.3875, ..., 0.3734, 0.4520, -0.2060],\n", - " [-0.6976, 0.2878, 0.0754, ..., 0.4601, -0.4200, -0.2361],\n", - " [ 0.1161, -0.0390, 0.1120, ..., 0.0925, -0.1058, 0.5641]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pretrained_embedding" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "6ea34c9b", - "metadata": {}, - "outputs": [], - "source": [ - "model.embedding.weight.data = pretrained_embedding" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "1332d9a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Parameter containing:\n", - "tensor([[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", - " [-0.0653, -0.0930, -0.0176, ..., 0.1664, -0.1308, 0.0354],\n", - " ...,\n", - " [-0.1329, 0.2494, -0.3875, ..., 0.3734, 0.4520, -0.2060],\n", - " [-0.6976, 0.2878, 0.0754, ..., 0.4601, -0.4200, -0.2361],\n", - " [ 0.1161, -0.0390, 0.1120, ..., 0.0925, -0.1058, 0.5641]],\n", + " [-0.9497, -1.5705, -0.5629, ..., -0.5853, 0.1596, -1.3159],\n", + " [ 0.6322, -0.5610, 0.4423, ..., -0.5541, -0.5787, -0.6026],\n", + " [ 1.1698, 0.1340, 1.5503, ..., 1.5039, -0.6415, 1.1412]],\n", " requires_grad=True)" ] }, @@ -1807,6 +1391,72 @@ { "cell_type": "code", "execution_count": 40, + "id": "5c1cbd5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", + " [-0.0653, -0.0930, -0.0176, ..., 0.1664, -0.1308, 0.0354],\n", + " ...,\n", + " [-0.1329, 0.2494, -0.3875, ..., 0.3734, 0.4520, -0.2060],\n", + " [-0.2301, -0.1799, -0.2485, ..., 0.5203, 0.6245, 0.1723],\n", + " [ 0.1161, -0.0390, 0.1120, ..., 0.0925, -0.1058, 0.5641]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pretrained_embedding" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "6ea34c9b", + "metadata": {}, + "outputs": [], + "source": [ + "model.embedding.weight.data = pretrained_embedding" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "1332d9a6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Parameter containing:\n", + "tensor([[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", + " [-0.0653, -0.0930, -0.0176, ..., 0.1664, -0.1308, 0.0354],\n", + " ...,\n", + " [-0.1329, 0.2494, -0.3875, ..., 0.3734, 0.4520, -0.2060],\n", + " [-0.2301, -0.1799, -0.2485, ..., 0.5203, 0.6245, 0.1723],\n", + " [ 0.1161, -0.0390, 0.1120, ..., 0.0925, -0.1058, 0.5641]],\n", + " requires_grad=True)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.embedding.weight" + ] + }, + { + "cell_type": "code", + "execution_count": 43, "id": "4fcb95e0", "metadata": {}, "outputs": [], @@ -1816,7 +1466,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 44, "id": "f8829cd4", "metadata": {}, "outputs": [], @@ -1826,7 +1476,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 45, "id": "7ed273e0", "metadata": {}, "outputs": [ @@ -1836,7 +1486,7 @@ "device(type='cuda')" ] }, - "execution_count": 42, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1849,7 +1499,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 46, "id": "3cdaf3b3", "metadata": {}, "outputs": [], @@ -1860,44 +1510,7 @@ }, { "cell_type": "code", - "execution_count": 44, - "id": "c721ad5d", - "metadata": {}, - "outputs": [], - "source": [ - "def collate(batch, pad_index):\n", - " batch_ids = [i['ids'] for i in batch]\n", - " batch_ids = nn.utils.rnn.pad_sequence(batch_ids, padding_value=pad_index, batch_first=True)\n", - " batch_label = [i['label'] for i in batch]\n", - " batch_label = torch.stack(batch_label)\n", - " batch = {'ids': batch_ids,\n", - " 'label': batch_label}\n", - " return batch" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "adf5afb1", - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 512\n", - "\n", - "collate = functools.partial(collate, pad_index=pad_index)\n", - "\n", - "train_dataloader = torch.utils.data.DataLoader(train_data, \n", - " batch_size=batch_size, \n", - " collate_fn=collate, \n", - " shuffle=True)\n", - "\n", - "valid_dataloader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size, collate_fn=collate)\n", - "test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, collate_fn=collate)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "id": "729aa9c8", "metadata": {}, "outputs": [], @@ -1925,7 +1538,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "id": "e0a80c30", "metadata": {}, "outputs": [], @@ -1951,7 +1564,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "id": "703aa1e1", "metadata": {}, "outputs": [], @@ -1966,7 +1579,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "id": "31343f1b", "metadata": {}, "outputs": [ @@ -1974,56 +1587,56 @@ "name": "stdout", "output_type": "stream", "text": [ - "training...: 100%|██████████| 37/37 [00:02<00:00, 14.13it/s]\n", - "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.12it/s]\n", + "training...: 100%|██████████| 37/37 [00:02<00:00, 15.43it/s]\n", + "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.76it/s]\n", "epoch: 1\n", - "train_loss: 0.684, train_acc: 0.604\n", - "valid_loss: 0.671, valid_acc: 0.682\n", - "training...: 100%|██████████| 37/37 [00:02<00:00, 15.06it/s]\n", - "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.24it/s]\n", + "train_loss: 0.681, train_acc: 0.625\n", + "valid_loss: 0.666, valid_acc: 0.704\n", + "training...: 100%|██████████| 37/37 [00:02<00:00, 15.49it/s]\n", + "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 17.17it/s]\n", "epoch: 2\n", - "train_loss: 0.648, train_acc: 0.718\n", - "valid_loss: 0.627, valid_acc: 0.729\n", - "training...: 100%|██████████| 37/37 [00:02<00:00, 14.65it/s]\n", - "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 15.88it/s]\n", + "train_loss: 0.645, train_acc: 0.721\n", + "valid_loss: 0.619, valid_acc: 0.739\n", + "training...: 100%|██████████| 37/37 [00:02<00:00, 15.24it/s]\n", + "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.93it/s]\n", "epoch: 3\n", - "train_loss: 0.588, train_acc: 0.764\n", - "valid_loss: 0.567, valid_acc: 0.769\n", - "training...: 100%|██████████| 37/37 [00:02<00:00, 14.81it/s]\n", - "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 15.66it/s]\n", + "train_loss: 0.586, train_acc: 0.761\n", + "valid_loss: 0.554, valid_acc: 0.777\n", + "training...: 100%|██████████| 37/37 [00:02<00:00, 15.52it/s]\n", + "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.60it/s]\n", "epoch: 4\n", - "train_loss: 0.516, train_acc: 0.807\n", - "valid_loss: 0.504, valid_acc: 0.803\n", - "training...: 100%|██████████| 37/37 [00:02<00:00, 14.80it/s]\n", - "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 15.93it/s]\n", + "train_loss: 0.514, train_acc: 0.810\n", + "valid_loss: 0.487, valid_acc: 0.819\n", + "training...: 100%|██████████| 37/37 [00:02<00:00, 15.56it/s]\n", + "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.70it/s]\n", "epoch: 5\n", - "train_loss: 0.446, train_acc: 0.847\n", - "valid_loss: 0.450, valid_acc: 0.833\n", - "training...: 100%|██████████| 37/37 [00:02<00:00, 14.83it/s]\n", - "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.03it/s]\n", + "train_loss: 0.445, train_acc: 0.846\n", + "valid_loss: 0.433, valid_acc: 0.843\n", + "training...: 100%|██████████| 37/37 [00:02<00:00, 15.24it/s]\n", + "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.74it/s]\n", "epoch: 6\n", - "train_loss: 0.388, train_acc: 0.870\n", - "valid_loss: 0.411, valid_acc: 0.844\n", - "training...: 100%|██████████| 37/37 [00:02<00:00, 15.40it/s]\n", - "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.37it/s]\n", + "train_loss: 0.389, train_acc: 0.870\n", + "valid_loss: 0.390, valid_acc: 0.857\n", + "training...: 100%|██████████| 37/37 [00:02<00:00, 15.61it/s]\n", + "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.95it/s]\n", "epoch: 7\n", - "train_loss: 0.343, train_acc: 0.886\n", - "valid_loss: 0.384, valid_acc: 0.852\n", - "training...: 100%|██████████| 37/37 [00:02<00:00, 15.13it/s]\n", - "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.03it/s]\n", + "train_loss: 0.346, train_acc: 0.884\n", + "valid_loss: 0.361, valid_acc: 0.863\n", + "training...: 100%|██████████| 37/37 [00:02<00:00, 15.37it/s]\n", + "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.98it/s]\n", "epoch: 8\n", - "train_loss: 0.308, train_acc: 0.899\n", - "valid_loss: 0.364, valid_acc: 0.857\n", - "training...: 100%|██████████| 37/37 [00:02<00:00, 14.99it/s]\n", - "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.12it/s]\n", + "train_loss: 0.312, train_acc: 0.896\n", + "valid_loss: 0.340, valid_acc: 0.870\n", + "training...: 100%|██████████| 37/37 [00:02<00:00, 15.57it/s]\n", + "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.57it/s]\n", "epoch: 9\n", - "train_loss: 0.280, train_acc: 0.909\n", - "valid_loss: 0.349, valid_acc: 0.862\n", - "training...: 100%|██████████| 37/37 [00:02<00:00, 14.62it/s]\n", - "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.37it/s]\n", + "train_loss: 0.286, train_acc: 0.907\n", + "valid_loss: 0.325, valid_acc: 0.875\n", + "training...: 100%|██████████| 37/37 [00:02<00:00, 15.31it/s]\n", + "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.36it/s]\n", "epoch: 10\n", - "train_loss: 0.257, train_acc: 0.917\n", - "valid_loss: 0.336, valid_acc: 0.867\n" + "train_loss: 0.262, train_acc: 0.915\n", + "valid_loss: 0.315, valid_acc: 0.877\n" ] } ], @@ -2062,13 +1675,13 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "id": "2d791c70", "metadata": {}, "outputs": [ { "data": { - 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\n", 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\n", 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" ] @@ -2091,13 +1704,13 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "id": "bc422190", "metadata": {}, "outputs": [ { "data": { - "image/png": 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ZvVqHRxIMRNPs7T+6atV779rCQzv76RxJWkt85k7AN509H7/LPmFf2B2/2sGXHtw14Wv/+sUeVjYHeft5C7jrqYOsveNBLv3Cw1Z1aiyzzyqeVsFqIJZmMJZmZ0/Eqqbt6I5weDhhVZCOvByZZcOCGmB8H9ZIIksqq4oBAxVCmKqEuWgMuhmMZayxE4OxNCPxLBcsqQOKVblwMkPI66Qh6DYep8Jatc8FwLxqL5qmsahBhbQPX7ls3LLr8SYhbIasXr2aaDRKa2srLS0tALztbW9jy5YtrF27lrvuuouVK1dO+hrvf//7icVirFq1ittvv52zzz573GOqq6t573vfy5o1a7j66qvZtGmTdd/3vvc9vvrVr3LmmWdywQUX0NvbyzXXXMN1113Hxo0bWb9+PV/84hcB+NjHPsY3vvENzjrrLAYHBye8pol+htWrV/N3f/d3XHLJJaxbt46PfOQjZc8ZGRnhLW95y9TfQCGEmAEf/8k2/ufR/Ww5OEI6VzjyE4BEJseH737B+mU+mUNDCXZ0R6zG+QMDxrJcSbD58u9384eXJ249MUVTWZ7eP8y1a1Q1ZnuXGtNgLtEtbQywel6oYgjL5Qv8ZMth/vBK/7j7AHpGk7zSE+GGs1r57PVr+frbNnDDhlY6hhL84oXOis8xQ1gsrZYN+4zZXQMly387uket96m12kskmWVvf5SP/WQb2fz49zucyNJW6yXgdlg/l8msgi2u9zMQTY8LzWYlrLFKhSpzyOqh4SSZfIGFdT5CXieHRxLW96r2OqkPGCEsmiacyFLjK+9LftOG+Xz6utVzXgWDU3E5cg699FL5hoD6+nqeeuqpio+NxdT/8bS3t7N9+3YAvF4vd9999xG/z2c/+1k++9nPjrt92bJlPPTQQ+Nuv+2226xdkaaVK1eWVdrM17vlllu45ZZbpvQzvOtd7+Jd73rXuNsff/xx3vSmN1FdXX3En0UIIWbKYCzNL7Z20VzloTeSYjSZxeO0H/F5j+wa4N6t3axtDVlN3hOJZ1SVyFw+K63CAGTzBb7xyF5es7aFy1c28o1H9/G2cxdY1ZhS5qDTN26Yz8O7+nmpa5Trz2plOJ7B7bDhc9lZ3hzg1y/2jHvuzt4o8UzeCjJjPbxzAIDLVzYC8Jq1LbxmbQvbu0a5e/NhWmt8xNJZbjhrvvUcs9plVsLMPq3BWNraGLCnP8bOXrXTcX1bNU/tH+L3L/fz0+c6+YtLFrO0MUgkleVbjx/g/ZcuYTSZIeR1Ma/aQ3c4yUA0zT1bDvOuC9qtULa+rZr9g3EVmPzqfVI7G7PU+Fw0Bj0AVtVsr7HTssbnYkGtj0PD6nVGk1mqvE6qvU7sNk1VzBIZq0fM1Fjl4V0XtFd83443qYSJGfWhD32I2267jX/4h3+Y60sRQpxmHtk1gK7D68+aBxx5qcz0R6OaNJXZUbFUeUA5MGSEMKMS1jEYJ5vXiSSz7OmP8YUHdvHb7b0VX2u7EcLWtVWzqqXKqngNxtLUB9xomkZbjY9wIms1n5vMStxoMkssPb4v66Gdfcyv8Y4LIDdtamNnb5T33rWFLz6wu+y+4bHLkUZv2GA0YwW0fEHnzsf2s7jez8I6H5Fk1mqY7wqrx3/vqYN85Q97eGz3INm8TrXPybxqL13hJD/efIgvPLCLG//7KWvZdf2CakD1oum6zu+29zKazJLJFazlyFLdxi7JWr+LtlovncPFcRnVPic2m0ad30XPaIq+SIqmKk/F9/9EICFMzKj//M//ZO/evSxfvnyuL0UIcZp5eGc/jUE3FyxRu7anEsIKBZ1Hd6sQZi4tTiZqVomMgGItRxrfa3dfcXSDObeqb4IjeXZ0jdJc5aEh6GbNvBAvd0coFHSGYhlqjYrQglq1s93shzKZIQygJ1ysBJnH/7xwKMwFS+rGjUS6bt085oU8VPuc496foZi5HFle7Rs0liMXG71Uo8ksf3vNSqq8TnIF3Qqv3eEkhYLO3ZsPAbDLWD6s9qoQ1h1OsrsvRpXHweHhBHc+th+P02YNUu2PpHnhcJi/+P5zfP9p1X9c43OWhaj6QLGiaDbcd46o72suRwI0BN08uU+FQHM22IlIQpgQQoiTXjZf4LHdA1y2otH6RRwZEzLSufy4vqMXu0YZjGUIehx0DB05hI1dqhs7qsEcSjqazFq3Vdr5B+roHzMgrGmtIprOcWg4wXA8Q50RNtoqhDBd19ncMUJbrReATiOEffq+Hdzy7c3ouk44mbV6o0oFPU4e/8Tl3HrBImLpXFkf1+AEy5HDiQx9kTTr5ldT53dx7qJarl7dRMh4n3cZy5NdI0me2j/EYWN58BXj9mqfk9ZqLyOJLC92hjl7YQ0/e/8FtFZ7WdFcZYWs/miaZw+ocLnFqJJV+5zUB1yYWfKMeSHreuv8LtpqfGTyBfqiKWuuGEB9wG0NgF1T8pwTzSkTwmZjF4yYefLPSQgxG/74Sj/RdI7LVjZa4aC00pPNF7jgcw/x1T/uLXve43sG0DR488Y2ekZTJDOTH+xsLUdGUyQyOXojKew2zVqO3NNvhrCcNTy00uHUiUyOfQMxVhsBwfy4o1vtRDQrYcUQVuz9OjSsdmS+fl0rUGxwf6lzlO7RJIlMnnxBt96HsWw2jWrf+KBaXI7MWz8jqLMWeyMp6gMufvzn5/M/7zgbTdOs1zdDZnc4yc+f7yLkdRL0OKxwVuVVIQygYyjB8qYgK5qD/PGjl3DXredYjff90RSbjRD2wqEwANU+Fw67jTrj/VhdMqqjxu+yKoUdgwmi6RxVxjWZATTocbCwzlfxfTgRnBIhzOPxMDQ0JL/gT3C6rjM0NITHc+KuzwshZtYLh0Z43X8+blVXZkMqm+dzv32FJQ1+Ll/ZaP0iLg1hh4cTDMUzfO3hvWWjErrCSer8bs4y+pKOVA0zlyNHEllriOrypiDJbJ5UNm8tR0ZSWWvHZKUjee7d2k1BhzWtKnyZs606huIMxTNWiAh5nVR5HBwqqYRtNqbpv2ZtizVXLJMrcGAwTiSZtcJf1QQhDLBCWDiZ5YM/fJ7vP32wZHdkcTmydIJDrd/N0saAVW0aG/K6wkm2Hh7hnEW1tFZ7rSphtdfFPCOEgTpMG8DjtBPyOfG5HATdDg4MxK0KmPnPrsb4Xg1BDzYNVhrHDzlsGkG3wwqpO7pH0XWsKmh9sBjaJjql5kRwSuyOnD9/Pp2dnQwMDMz1pYgj8Hg8zJ8//8gPFEIcd7qu8+n7XuZ161o4e+GRT9eYisd2D/JS1yj7B+KsnT87y0J3PdXBwaEE33v3ObgcNqo09autNISZgSCTL/Av97/Cne/cCKiA1Bh0WyHowGCcVS0T9xDFSoaTmktnZy2o5pWeCIOxNB2DcdwOG+lcweoFG9sTdvezh/jkz1/inEW1XLRM9a/53Q7q/C529kZJ5wpWJQxgQZ2vPIQdGCbkdbKyOUhzlYfucIoDg3Fyxuwus1F9okoYFANaOJHlwZf76I+krXMWS0PY4oaANQOtLlC+w7PKU3x9TVNHHg3FM1y3rpVMrmDtoqz2OanyFuPG8qbyzQIAV69p5qfPd6Lrqu9r0OhPM8dLNAbdDMfd1k7JGr8LTdNorfaiacUzNs1w2WCE2BN5KRJmOYRpmnYN8B+AHfimruufH3P/QuBbQAMwDLxd1/XKA0wm4XQ6WbRo0QxcsRBCnL4SmTzfebID4KhD2IHBOE/vH+It5ywAipWl3kiKtczOL8QXDoVZ3ODnomUNADjsNgJuB5FkMTCZIezmTW385LlORhNZQj4n/dEUjVVu2uv8ZY+bSGlF7w+v9OG0a2xYUMMPnznE1sNhcgWdDQtrePbAMAdLhot2h5P84oUu/uKSJfzf4wdY11bN9959Dm5HcYRGW62PrYdVJaiuJIS11fjYVXIA9uaDw2xcWIPNpkJIVzhZdkD2oSmEMLNi1DmSIJMr8PyhEXRdhal4OkcuX2AwluaCJXXFEOYvD2Glr7+iKWiFrjWtVfSMJsse53LYsGmgw7gdmwB/e80Kfre9l1g6xxs2zOfOx/ar6zQqYW87dwHd4aTVmF9r3O5y2Gip8vDi2BBm7KicreA/U2ZtOVLTNDvwNeBa4AzgLZqmjT2984vAXbqunwl8BvjcbF2PEEKIyZmVkEPDCQoFnd9t76EwzbP17t6sqjwZY1CquXOud7TyPKuZMJrMWr+UTSGvc1wlLOR1cuPG+eQLOo/tUSsn/UYlzO920FTlPmIIKx0H8WzHMGfOr6bJ6GkyRy6cvVBNiD80pMJQQYdvGEcIfe+pDvb0x7hu3byyAAYqhJm9X6VN9QtqfXQOJ42dk2n2D8TZ2K5Csjl/a0+FEFZaqRrLDDdmwDKraPNCXmLpHEPxDLoOZ5RUBevGNPqXhrANxs8MaonVbLZ32jV8LjtOu42mKg9tNT58rvH1n8agh9tfdwaXrWjgkuUqTPtddlwOFVNevbqZWy5cZFUIa/zF733Wwhr2G7tUzWvasKCG9W3VnG9M1T9RzWZP2DnAXl3X9+u6ngHuBl4/5jFnAOZ00Ycr3C+EEOI4MZvLDw8neHTPAH/x/ed5ev+Qdf++gdgRQ1k4rl5jNJlF13UODKhf8r0TjGkwDcczjBg9SdM1msyOq/oEPQ5Gk1nrsOcDg3EW1ftZ31ZDjc/JQzv7yRd0BmNpa4lrYa3fCjCHhxOkc6pBvfRIomg6x7yQeryuw8b2Gqq9KhiYzeTr5lcDlC0h/uEVNUH/87/bCRSHqJZaUFvsmypdjpxfq3YA9kfTVs/UOYtU6JlX7aV3NGVVoUq/71QqYWOPW1pQ6yOdK1jN/u31fjxOFRXGVsKCHoe1a/Fs42ii+oCbxqCbZuM9CnldVk/WhoU1XLh04lD05o1tfPvWc6yl4UoDbqt9Lmxa+fvz8VevwGUMkw15ixsafvmBC61/tieq2QxhrcDhkq87jdtKbQPeYHx+AxDUNO3Ejq1CiNNCvqCfdpt9rBA2kuCVHjXjyRx/0BVOctWXH+WeLYcnfD4Uq2mjySzD8Yx1wHPvaOUxDaYP/OB5PvqTbeNuz1U4CsdUen7h2Cb0kNdJJJnlQz96gVu//SwHBuMsrvdjt2lcuqKRR3b1MxBNU9CxdufVB10MxzNkcgWu/spjfOeJDp7ZP8SVX37UmssVT+dYWOe3GtY3Lay1lsBe7o7QVOWmxQggyWze6mnqGU1h09TU98X1fitolGqrKe7iK+2/WmI89vG9g/xqazdep91q6F/SECBX0Hlk1wCLjcdNJYSZ79e+gfEhDIrLyI1Bt7W0N7YnzGbTCLgduBw2VhujNta2qkZ4M4RVlxwZ9LW3buBzbzhzwmsyNVd58DhtZc812W0aC2p9LKgtvn/t9X7ee/EinHbNutaTxVzvjvwYcImmaS8AlwBdwLj9wZqmvU/TtC2apm2R5nshxPFw0/88xb89MPHhyKciM0ClsgWe3KsqYL3GdPLdvVEKOjywo/L0d5MZ5EaTmbKdhr2RiZcjc/kCLxweGbcz8ZWeCGfc/kDZUpvpoZ19nHnHA4wmskQqVMJCXieRVJZXeiJs6xylZzRFuxFSLlvZyEgia1WnzGpJrd/FkHFodSKT55WeiBVGzfcjlspR5XVYS3Mb22usQJPJF1hU7y+7luXGTkCAd5y3EKhcBYNiAAKo8xfDxHmL61g3P8Sn79vBb17q4X0XL7aWMl+/fh5vPXcBmXyB84ylt0NDCTRNVaomYrdpBD0Oa/nVDI4LjHEO5vJeY5WH+oAbr9NecRkx5HXSXOWhtdqL3aax1qgCNld5rPuny2bTWNoYGDcp3/Sz91/AX1+5rOy2j161goc/dulRfb+5NJuN+V1AW8nX843bLLqud2NUwjRNCwBv1HU9PPaFdF2/E7gTYOPGjafX/5oKIY67bL7AC4fDFYddnsrMCe8AzxwwQpixjGj2dj25b4hkJo/XVflMRjPIhRNZa+7UqpYqK8xVsm8gTipbYGDMKIfnD42QyRfYejjMnv4Y33vqID94z7nYbBpP7x8mnslzeKR8PpQp5HXSOZIs6+Eyq08bjf6lP5ohzKiE1fndhJNZazej2UcGxQn1sXSOgNtJU5Wbaq+Tap+LQkHHpqner0X1gbJrWdoY4BljF+V16+dx7doWVjVX3n1pjlvwuexl76/NpnHHdau54etP0lrt5f2XLrHuc9ht/PP1a3jdmfNY0Rzkh88cYiiuhs/abJOPZqj2OTk8nMOmwUXL6vnpc53Mr1FLorv7otg0aAq6aQi4GQhUrmRWmyMmPE7uft951ggJM4RVH2Uo+spNZ1lLjGON7U0D9R7Nrzlx54FNZDZD2GZgmaZpi1Dh62bgraUP0DStHhjWdb0AfBK1U1IIIebU4eEE+YJuHdZ8JF97eC9LGgJcs6Z5lq9sZqWyeT71i5f40OXLWFTvZyRROtxU/f+uGZ7M2VrpXIGn9g9y+cqmiq9pvkY4keXAYByHTWPjwhp+/rza+D4cz3DHr3bwT69fQ8hYbjLHC0TTORKZnFVx2devvmfHUNyaxr6nP8aK5qC1G/DAYBxdH19xqfI6rQDWGHTTH01bIawl5KHW7+LJfUPW/aCW23Qda/7X/sG4FaiePzRCLl8gls4R9Dj41LWrrF4nm00NLh1JZFlU76OqpAJVH3BTH3AxkshyRktowvBqXpfdppX1O5nOWlDDf77lLJY0BMYdSq5pmtWA7nHaSGULU6oIVXtdHCZJrd/N+y9dyvlL6qyfd09fjOYqDw67jQ9dvoyBWOUQ/clrV1nN85vaiztqq31O3A6b9c94uirtoDwVzdpypK7rOeCDwAPAK8A9uq7v0DTtM5qmXWc87FJgl6Zpu4Em4J9n63qEEGKqzGWxxBGmp5v+90/7+ckReqWOxm9e7GHb4fCUH//wzn7+33c2H7F5/ufPd7KlY5hHdw/w8+e7eGSXOjsxnMjidpT/WjBD2IHBOCubg/hcduvA67F0XbeqaeGkCmELan3Mr/ESz+SJprI8urufX23rZltnmL39Mb71+AFeMkIYqN2K33uqg/0DMatf6cBg3BqCalakzKBkLqdVjVl6Kw0hn71+DTdvamOFUaXRNI01rSHSxg5Oq+fJWAI0x0FEUzm2HQ7jd9lJZPK83BMhls7hd9u5YGl92c47s4l8UX3AGpGhbnfSGPSwrDEwaQADVdWaV+2pWOkBeN26eZwxb/JzEM1NAlMJYeZjGow5aTecNd+67o6huDVgde380ISh+8Kl9WXhy6RpGh++chlvOEvmQk5mVueE6bp+P3D/mNtuL/n8p8BPZ/MahBBiusx+mKlMeU/n8oQTWbrCMzuCQdd1PvnzF7lkRSP/+ZazpvScp/cP8dDOfvYNxKyp5GP1R1P87U9fZGljwNrFZx47E05kaAi6yeTUTrxVLVVlS3Ob2mtY2RzkZ8938peXLbWOojHFM3lr1MFoMsvhkQRttT6rSbsvkiqbKP/kc0P896P7CHocOGwauYLO7r4o/3DvDm7e1GaFsH39cSsYb+4Y5vqzWq332wxhlXrCAGwaXLqikVevLq9SrplXxWO7B4yKjQpHZgVqV8lOw0gqx5s3zueeLZ38ac8g+YJOwD0+4JgVJLPaFjIqcSGvk49fswLHEZYGTa9f13rEsDaZap+T3khqaiHMZx7xU6y8+Y0qZEGnbMr90fjLS5ce0/NPB3PdmC+EEFNSKOgcnMIByzPB/MU+lUrYQMm5eTPJ3FlY2qd1JOZORHNeVSU/e66LXEFnZ2+Ue7epNl3zoOaRRIZqn9NqEL9oWT1D8QyRVJbu0SSL6gN87OoVAPzLb14Z99qlIyZGExl6R9O0hDxWf1DPaMpqso8kc9Ycr2gqx8Z21adlTqF/dPcAXeEkdpvGrj41Rd5lt7GlY6SsUX//EUJYW63PWi4rZe4uLG3+NsPIzt5I2WMvWFJPa7WXx/cMAhCo0PBe7XVi04rN9WZTfLXPyWUrGq1BskfysatX8IHLjj68mGFwshlhJrNfq6Gk8mZWwuDYQ5g4MglhQoiTwh939nP5lx4ddwTMWLp+7KMlzKrLRJWwQsn4CrOKFEnliKayFR9vyk4ybmGiaygdOHok5vd//tD4EFYo6PRHUvx48yHWtobwOu2ksup6zIOaRxJZanwuVrYEWdLgZ2mD6st5dv8wug6LGvzMr/HxvouX8JuXejhcMgcLijsjAQaMXYbNIY9VCesdLa+ERZJZvE47mgbXrmkBsJrYe0ZT6Dpsai8OAX3N2ma6wkke2aV2ydttGvuNatnY3iPzmJxKoyCgeJxN6RwpsxI2GMvQVuu1qleL6v0sbwrwYmcYgIB7fKWqrdbL8qagFfjMEGjOrTpeqq3vO4UQZlbCSoKov+Rna60+sWdsnQokhAkhTgq9o0lruOZkPvqTbXzoRy8c0/c6YC5HVmjM13Wdi/7tYX7wzCGgWEUCFRwmsrM3wurbH7B+kR+JuSRaGmyOJGpUwp4/NP57/MO92znnX/5Ix1CCWy9s58/ObMGmqXMPB0qWI6t9Lj71mlXc8+fnW+HpKWNg6yLjaB9zonnpgFAo7oyEYs9Wc5WHpioPmgYv90Q4PKKCWySZZTSZZVVLkMc+fhlvP28hLruNHd2jZa955apiL9LbjBEP332qA7fDxtKGgPUzj638mCHEPI5orLZaLzU+J/NKgoY5CNS8bnNcQ3u9n+XNQeJGZbTScuSnXrOKH773vHHf/3iPTDCD1VQa4s1rK12OLK3ySSVs9kkIE0KcFKJGVepIS4Qvd0d4uScy6WOSmTzXf+0JtlZoek9m8nSPpnA51C6z/Jgm92g6R1c4aU1HH4gWg1fXyMRLkk/vGyKTL/CbF3smvTbTsVTC9vbHypYxI6ksP3u+k0uWN/DlN6/junXz+LvXruJH7z2PVS1VxZ6wZJYaY+RAXaA49dxs3G+vV6HEPIDZ3KHYMRjnmq88xnYjQM0LeaxlwuaQB4/TzoVL6vnhM4cwi5SRVJZISg1Zbav1YbepQZsFXS3pmdPZzZlardVeNi6s4c8vWUw4kWVpY6BsMOf45Uj1/MUNlUOYpml8793n8pGrVli32W0aNUaDfX3AzeJ6P3V+FyGvk+WNxR670iU7k8/lKNvVaC4LVho4OpvMDQJjNypUfKy3+LOa3A47TrtKoq01EsJmm4QwIU5mXc/Bbz4Khakvc52sYkbF40jN8oOxDIPRyatlh0cSbD0c5ql9Q+PuOziswsMqYyddYkw1zDyWx1yK6y/5XpM157/UpYLhQzsr7ywcy+xLi6Sy44LgRKKpnDWhfUtHcUnyV1u7SWULfPTVy3nDhvk47DaqfS7OXVxHU9DDcDxDKptnNJktOyrGDGH7BuJcsryBoFFtCnqczAt5rN6sX23rZmdvlPu2qYDZXu+3rtl8jZs2tVm7EZ12zeoJKw1PZqha3ODn0hWNLG0M0F6njs1Z3hRA0zQ+ee0q/ucdZ3P7n51hTXB32NT5hKWWNPi5/c/O4PXrxx7UUrSmNWRdn8l8zfqAm7+6Yhmfe8NaoHzoaqUQNtZcVcKm832LjfnluzH9xs8nlbDZJyFMiJPZSz+Fzd+E0Zkfj3CiMcNXPD1xJSxf0BmOp4mkcta5f5WYQ0RL+8vu2XyYA4Nx+ozlxSVGP9TYypu55GYuq/VH0tQHXDhsWllz/nA8w38/us8aF7GjexRNgz39sXG9VACZXIFvPLKPvf0q2JjLkbrOhL1mL3WOllXWYukcFy1roKnKzX8+tIdCQSeTK/CDZw6xqqWKtUYzeilzUOn+ATVvq6akchN0O4zDlzVuf90ZZc9b1hS0+rvMYGlOl19YVxyaaTblv3p1EzU+J067xrLGIJFUdlwIM5vkF9X7+czrV3P3+87DZtP48BXLecf5C63HXb26mXMX11mVp5DXac3sMmmaxv971aJphyBzTEV9wM2Z86utXZVLGwPWOYmVGvPHeu2ZLXzwsqU4Jxg4OlvMn3fs8NpKzl1Uy41nzy87fBvUDsmg2zGl5n5xbCSECXGy0XXY+Rso5GFor7ptYCcM7Ia+HXN7bbPIXI6cbIDqcDyDWTQajE28q9AMYeYMrEJB5xM/f5G7Nx8iYiz/tRi9Qmb429sfY09f1AphvZEU6VyevmiKpirVfF4awn61tYvP/3YnL/dESGXz7OmP8Zq1qvl8bDUsls7xzm89w7/+bif3bOk0doImrJAxUV/YnX/az9//8qXie5TKUR9w88lrV7Gtc5Q77tvBO/7vGV7pifDnFy8eF1SgGHx29akAVVNSCdM0jWvXtPDRV6+wQqlpeVOAfQMx+iMptpX0uQXdDivIeJw2KxS4HXY+ePkyrl/fSo3fSbjCcUNmIFxU78fvdlgVmvdfuqTinCrz/qkEjqmqNSthwfKGeq/Lbp3tOJVK2IYFNdZO0uPJ6gmbUmO+iy/cuG7czxNwO6QKdpxICBNiLug6DO1Tn6djEOme+nM7N8Pdb1VBzHyN/pfhl++HX/z5zF/rCcJcjkxMshxZ2rQ/2ZKkGcJ6jEpYOldA12E0kbV6sFpC6peQWQn7x19t55M/f8m6X9dVD1h/JE1j0E1rtZfucLGyZi4nHhiMs7M3Sr6g87ozW2it9o4bIfHrbd08vX8Yl93GYDRNXzRFMptnfVs1MHFf2HA8zUgiSyqbJ1/QrWnur18/j0tXNHDXUwfZejjMV25az/VnVV6WM3cH7uqtvMvwS29ex19csmTc85Y1BUnnCtz11EF0Hc5frAaXVvudVhBorvKUBb93v2oRX7hxHVUeJ72jKQp6eUO9eS0T7WgcywypMxnC6v3j+6RMZi/cVELYXFnepAbqjg3N07GiOchZC6pn7qLEhE7cv0lCnMp2/hp+/A74wDPwzH/Dvofgw9um9tyosfzU+SyMdKjPu56Hnq2g2SCfA/up9692zKqETbzMWBbCJtlFac6z6jMqYamses1wSQibN6YSNhTLEE3lymZhHRpO0B9Nc+b8EJlcwRqvAMX5VR2Dces1V89TPUgDYwJix1ACl93GiuYgA7E0HYNquXLd/Goe2tlPeMIQpm7vj6St8BT0ONA0jW+9axOjySwep33S4Z9m9clssi+thE3G7JG680/7mRfycNOmNp7aP0SNz2VVYZqqKo84qPI46TM2NJRWbBY3+HHYtLL+q8nUlSxHzpRao4rXUOHw6PVt1Ww5OILHeeLWL5Y3BXn5M9cc02t8dYrDgcWxO/X+Sy3EyaD3JUCHA4/B/kcgfEg119um8B/3uJqRxMu/At0IJLt+CwWjQjS8HxqWz8ZVz6m4tTty4kpYabiZLIQNG0uK/dEUuXyBlNE/Fk5miCSzuBw2K4yYlbBoKsdANM1wydJgx2CcobiqhBV0tUSZyxdw2G1llbDOkSTVPifza7zUB1xWv5fp8HCC1hovTVVuusIpeiNqWXNViwojEw1sNQNhz2jS+qtjVpZsNo2aCmcQjlXnd6FpWKMz6qbwHIBljQEcNo0an5M737nROs+wuiSEtYQmCGFeh7VLsrSK9Zo1Laz/ePWE4W3ctQdmPoQ1h1T4aq5wDe+9eDFv3thWcVlXiKNx4sZ5IU4kP7wJtv8Mor3w368qLgNW8oM3q4b5yZjPf/leFZr0AiSNJardD8I3r4LUBGMW4saOvvBB9bF1IxRKKiUD4yeZnwqiU2jML6+ETdwTZoaXgq4eZw4tDSfU2ISQ12ntEDN70CLJLJl8gYNDcYJuBy6Hja2Hw+g6NFR5mF/jJV/Q6RlNkcrmrZ2S+wfjPHdohA0LatA0NYZhbEA0j/epD6j7zHlj5lmHkQqVMF3XrTDZG0lZ87Km0jReymG3Gd83w3mLa5k/xbEEfreDe/7ifH79oYtY0xpicb0fv8tOrc9p7bBsmiiElSxBlgYom01jfo2v0lMqMnvPpjKOYapev76VH7zn3Io9UW6HncYpBkQhpkJCmBBHko7B7t/Brt/B4WdUFevgk5Ufm0nAngdg248mf02zob7jT8XbzApXx5/UUuMjn4M/fQk2/1/5c83HmVb9mfpYuwTQoH/nlH6s4+1/H9vPAzt6j/r5Vk/YJJWwwVgGt8NG0O0Yt+RXqrSa1TOatHZSmgNEQ16nNfIgkc5TKOjEjO+7qzdKbcDF/BovT+9Xy4+NQbd1XM3h4QSHhxPougoYu3qj7O2PWcfy1AfcjCSyZdPzDw0naKvxUh9wMxzP0BNOEfQ4rL60/miaj9yz1TpLUb0PeTLGyIe+khAWPIpA0hh0Y7dp3HHd6mlVeTYsqLGW7Ww2jS+9eR3vuWhxWU9YJaXVL3Oy/dGonYVKmMdp58Kl9TP2ekJMRpYjhTgSswdraG8xPIUPqiXEPQ/CpvcUHxszQsbhZ9XuRVtJL46uw1P/BWvepKpfTj9kS5alEoPGa/Spj09/XX0MzoNN7x7zOA3QwVMNCy9Uty+5XC1PnqCVsG8/cYDVrSGuHnOQ8lTFp9ITFk1TH3DjdtgYOEJPWFOVm75Imr5IimYj7ISNEFblcVgHGcczOaLpnLV8tn8gzqp5VdT4nDyya4CWkIeNC2usZcvDIwmranfJ8gZ+tU1tutjUXgsUG76HYhmaQx4iqSzhRJYFtT7cDhv5gs7O3ggtIQ8uhw2/y85juwfY1jlKtddljYoYLulN6xlNsaRBBcvgUYwVeMd5C0nnCqxsrpr2c0tdYxw9lM0XeMs5bWXT7kuVBq9jCVBBt4P3XbyYa9Yc3d8pIeaaVMLEyW9wL/RuP/bX6Xq+8jKjuXNxaF/x/pEONZ/rNx+FRLEZm6gRoNIRtWOx1MAuePDv4Y+fVvevuUHd7jP+r9uscMX6oG4pzD9HBaxoN8RKRhrEB6HlTNWEX7cUmtZA23mw9k3QsPKErYQlsnlGp3EET6l8QbfC12S7IwdiaeqDbrW8doTdkataVOAwlw9BVZcGoxlVCTPO0Etk8mXLgZl8gWqvk2vXNHP5ykbu/cCF1AXctIQ82G0ah4YTVj/YZSvV8T4uh40z56sZXWYIG4yl+eMrfdbxPm21PusMvx3dEasvqtrn4sUuNYn+4V3FvwelRwT1RVLWxoWjqYTdfM4C3nVB+7SfNxGn3cbn3nAmbbWVlxYnWo6cLk3T+NRrVnHm/Oqjfg0h5pKEMHHy++3H4ftvVLsCx4oNQKr8LLqy8RClfvYe9Tq5Mb+8zUpYelQtRwKMHCyGHbNyBcVKGMChp8tfx6xQmf1iq14P1QvgrLerr+NGJSzap8LUe34Pl/2duq2nZOdkfBBCbSqgLTgPXD549wPq84aVqlqXP7qwM5sSmXxZcJiO0tlgk1XCBqJpGgIu6oOuyXdHJjIsaQjgstvojRRDGKhKVsjrxGW34bBpxNM5ImOGpdb4nNy0aQHfumWT1SPksNuYV+3h8HCSjsE49QEX64xwsG5+CLdDhTpz+e6ZA8O8+7tb+Ptfqv+BWGD0hIF6r8ym9iqv06rCHRiMWwdWm5Uwn8tOz2iKyDEsRx5v5nKkTTuxxz0IMdskhIm5pU/tOJZJjXSo8LPnwfH3ff8GuP9vy2/b9Vv4z7OL4x1A7UwMH4KRA2rJsFSkq/h56XKkGarMkAbFSpi7Cg49Vf46Zmgzm+jrl8GHX4TL/0F9HS9ZjgwYyzjN6sgUerYWXyc+AP4GeNd9cPU/l3+PxlXq9Yf3cyLJG5PbR46iEqbrutUPBkfuCWswK2ETNOansnkSmTy1fhdNITd9oymrMV+9ft6awO5z2Y1KWPn3rJ5gjMOCWh+HhhO80hNhcX2AtlofQbejrMeowQhaT+xV/7zNKfNtNb6ysQhmP1W1EVhWGGMbzEGvZqBd2RykbzRlTdU/Gaacm9dYVWHSvRCnEwlhYu786Utqp+GxKBRg1AhJz3+3/L5sSk2Q732p/PbeFwFdVbNMsT4VXpx+eOxLkC05AzAy5sBlT7V6fPiQ+jo6phJmc8LSK6HjifKQOfCKCmegHlO9ADRNzfTy1qher1wGksPFEOapUg333VuNnzev7vfXQ6VfXnVL1ccTLISZwSmcyKBPI3iPJrKceceD/PpFtSRst2kk0nn+/fe7uf5rT5Q91jyyqD7gpiHgZjSZrXh0kVlBqvW7aK7yWJPvS5mVGr/bQaxiJaxyCGur8bGnL8qLXaNcuLQep93Ggx+5mPdfWhx2ak5if7ZkpliVx0HI5ywbEGruLDSb3K9e3cTypgB3PXWQg0Nxa0aYeQD3aDKL067hdpz4/1k3q3XH+1xFIU40J/6/reLU1f0C9G1XOwqPVmIQ8mnwN6pKWGnv1OBuNfpheH/5AdfmUmTpLsPRTvVx7RtVs3zp8T+RbqhfDjZj2WTJZeXXULoEGe1VAWrpFer20gDYvxPaX6VCVe2i8qZ9f4O6nrhx/cGShuaWddDzovo8OaJ+Jn9D5fejdnH5z3iCMJvWc8ZU96na1Rclms7xpz2qatQQcBNL59jeNcrO3vIRHiMJdWRRfcBt9VYdHh5/oLYZwmp8Lqo8TmLpXNlyJBTDgd/tIJHJWT1hrcbYghp/5fDQVusjnsmj63D5ykZATd43lyIBfC51HmMsnaO1Ws0NM3unqjwOXMZZgy1jQtjq1hD/csNaIqksb/j6kxwcimM3BpvmCjodg3GCnpOjsmSGXAlh4nQnIUzMHbPCFD44+eMmYx5cfeabVTgpDT0DxvJfLqm+x70fhL6Xi0uKpYHNfJ2VxriH0uW/aLfqwappV18vuaL8GkorYdFeFaCWXqW+3vOAcQ0ZGN6nlgtf9xW4+nPlr+GrV/O/zNcKlOz2alkHo4fU9ZrB0T/BFnpfrarUHcdK2LbDYe741Y5JK1ylh2BPdA5iJQcGVf+TuWTXVOUmkcnTH02TyhZIVnjdap+Ti5bV43bY+I8/7hn3muYyXq3fhc/tIJHOk84Vyh5jVcJcduLpvDX+YWljwPgeE1TCjDDVGHSzet7EOw3NZcdVLVV8+c3rue3alYBqNK83xi6Yjfkhr/p6TWuIje21fO2tGxiKZ/jd9l5qfE4rrO3ui50U/WCg3lebdnIsnQoxmySEiblj7jocOZYQZlSwFl+qPpZWgPpLRjW89BN44XvwwvdVGIJi1an0ddrOBW9teSN8pAeq5hlLfVqxEmZ3Q82i8kpYrE8FqGATzNsAux+A576jjiYq5KBhFSy6GJZdWf5z+OtVwDJfK9BYvG/51Won5GNfLPaN+SYIYaCqYcPHrxL2m5d6+M6THXSPpiZ8TGkf13RCmHn0j9nf1VjlIZ7J0Wec+Thc0uhvLhlWeZ3Mr/Hx55cs4b5t3Tyzf6jsNYvLkU4VsjITV8J8LqMSZrz24gZ1pmGNr3J4MGeFXbaiEZtt4oqUuey4vCnAxcsbuGhZsbJpVvHMGWHXrGnm3a9axDwjbJ29sAaX3UZ/NE2Nz8Wa1hCappr2T5YQpmkaVV6nVMLEaU9CmJi+1Kg6budY5HPFXYWlDfLTZYan1rPBFSgPHwM7Va8VqPAF6sxGc7dkbEwIc1eBt1pVnswerHxWXWfVPFj1OljzRqhqBadPLVFWtVauhIEKT52b4b4Pw++N5vvGlZV/Dn+DWlo135NgSSWscRWcfasaiXHg0eLjJ1K7+LhWwrpG1JLf7r4o+wZi7DHOICxVWrGazg7JA2OO92kMutF1NbwU1Lyv7V2jdI4krCVDs7ry/kuW4HXa+e328gGxI1YIc6uQlc6XNeaXvobfrSphkWSOoNtRXCL0Vq6ELW8KsL6tmjdvapv05zKrXeZE/PL73MaxSeoa1rdV8w9/doa1zOhx2q1xF7V+F/OqvVxshLig++QJNdesbuaiZTIUVZzeJISJ6Xv+Lrjr9cVjdo5GvL947uGRliMj3cVG+eH95c3u4cMqfHlrVPgwlxpBVcLaL1KByfwepd9r7HJkyPjFOW+9em4ubYQiXYWws94Ob/o/1RDfdg4sukgFLnN3pNVUbwSoM64HVxAu+QSceZN6/bpllX9Gf72aNxbpBrTxIevyv1dN+o//e/HxE6lbokLl2FEbs8Q8nmdPX5SP3rON237+0rjHJI4yhHUMlYewsWcKDsczvP8Hz/GlB3dbS4YhYxCo12VnYZ2PzpHynsPhRBZNwziaSFXCktk8DptmVWYqVcKCHgdrWkME3Y4Jj/bxuRz88gMXcvbCmkl/LrMStqxxfAhb0xrizNbQpL1dmxapwa+1xjmPNxuhb7pHFs2lz7/xTG4+Z8FcX4YQc0pCmJi+SI/qvzKXE4/2NUyTVcIKBbWD8k9fUv1eXz2r/Kif0cMQmq+CUd3SYgjLJNTrNp5hHOcDhEr+g1+3dMxypPE6oCphhawKYuZ1BueVX9c774Wr/0UFrlifCoZjq1iNK+G2Q3DZp+ANd6pxFM4Jzp3zNwC6+p6+OrCPqWj4atUoi0IO0NSS6URqF6t/PseyzDsN3UYI23Z4lJe6Rq2lwlJH0xOWL+h0DCXKjr5pLBnhAGrgaXc4xUA0bS0Zlk6Mn1/jG9ecPxLPUO11Yrdp+FwOCrq6Jo/TbjXBh3wllTBjWGuV18kFS+p56dNXT+lg7MksqvcTcDus5c1SH7lqOT99/wWTPn+TcQSSeR1XrGqiucoz4YHZQogTk4QwMX1mc/gxhTBjrERV6+RhIdoNiSE1zb77BXXbwK7i/aOdxfBUt1SNjchljKZ8XQWhOmPH4PkfADS1y7F1oxrkWul1Wtarj0/9F2z7ofrcvK+UpqlKWDYB6WjlpUSbrfLnY/nq1Me+7eXPL3X2LWpumL9BjbWYiBk6j8OSZDqXt5YGf/9yH/mCzlCF+VzJbLEnbKqVsO5wkkyuYC1ZeZy2cT1Ee/pj5As6I4mMNcurtNnbnNtVumlgOJGxwovfmIo/HE/jcdqsmVzmgdBquVJVwmayifwd5y/k9x+5GI/TfuQHV3D2glpsWnHmmMth474PvYpPXDPBcrcQ4oQkIUxMn3nG4bGEMHMJb8H5qmI1dmfdo1+A332yWNnqf6XYaG/uZITxIUwvqNc7/Ky6bf6m4uys5a9WR/zUtKvlxXi/qrSlY2pp1Xyd2kVqCfGln8CWb8E5f676sioxlx5jfcX3w5zxNR3m8uNIR3lTfimbHd7yY7j5B5O/ljmm4jiEsL5RFcBCXicZ40DqZDY/bqBqPF25Eraje5Trv/bEuLEVH7lnK3/z460AXLxcvTcBtwPfmOnq5q7JcCJLJKXmZHmcxf+stdV6SWbzDJWcszgSz1Br7G70GedDDsczuB12Qj4XdptmTXFXjft5wonsMR00PZbbYbca749GyOfk7vedzy0lRw01BN34Zfq8ECcV+TdWTN9MVcLsLtVQv/2nqtpV2ue07UdqJ6AZKKLdxSODzGb8bFIFQiuEmRWgfWpafahN3Xf2LRBsUTsZr/kc5FIq3BVykAoXf55QSTP1ZZ9SVadMAtbdNPHPYTbhR3vVWAvNribhT9f8jXDO+6Dj8eJ4i0pCrerPZHy1avk0G5/8cTPA7Ae7aFk9v36xuMQ8FMvgqy3+58VszK/1uwiXVMIe2z3I1sNhOgbjrGkNWbc/smvA2sW4qb0Wj9NGwO3A7ypWjgJuBy93myEso5YMx8zJMncrHhpOWH1Yw/GMNUrCfL3heAa3UQmr8jis11hpnC+5szfKGS3Hdrj1TDtn0SRL0kKIk4KEMDF9cWPLf7RCCOvZpipW626e/DUiPWrZrXaR+nqkAzJxdaTQ+rcUdznue6j4nK7n1EczhJkT681eLzOwDe5RIWzRJerr6gVwznvV54suUh9Ld0iar1M9ZkfbqtdN/jNAsRIW7YVDz6jg5hrf53NETi+85gvTf14lmgYfebnyRP0ZZoawS1c08usXe5hf46VzJMlwPMPT+4fY2F7Lonq/1RPWEvKUHV1kzgEbLqlUmcuL5yyqZX1bNU1VbhbU+nA5bFalJ+R1Uh9wsc/YPRnP5BmOZ6z5XiYzbB0eTrBhQY31vcwzHc3K2nA8Q0vIyw0bWq2DvUGFS4dNI1fQx722EEIcK1mOFNOj65NXwu7/ONz7ARWoJhPpVtWaBqOHpes5eOIr8LtPwLYfFx+39w9q+GgpM4SZvWRmkPPVqt2HT39dLQ8uOG/i728u+cX7izsmaxZNfs2VVC8Ahwc6HlM/w2Tf83g6TlPTzab8K1c1ctaCamt5rCuc5OM/fZFvP3EAgEQ2h8thoy7gLquEdQyqnYulfWIjiQy6Dq9d28KnXrMKTdN4zdoWLlnegN9YPmwMuq2dgaaDQwmrl8vUVlMMYaDOoRwp7QkzKmHhZBaP08ZlKxrLjhgKepxWxWnsawshxLGSECamJx0pHkA99kzF/p1qybCQU4EkNgCpyPjXAFVFq5qnAlTdUjXUdLdxAPcTX1EfNRvkM6p65VS/TGlZp/rJ8tnirsrqhcXXffVny/vNJuI3QlisX72OwztxL9ZkXD5VMXvh+2oyf9u503+Nk1h3OEl9wE21z8Uv/vJCrl6tKoPbDocBNUAU1HKkz2Wnxucsq4SZw1hLm/nNz+sCxZD111cu5+NXr8RnNNI3VXnGnd94aDhRtjMS1JiK+oDb2iEZS+fI5nVq/cURFKD+32KiJnnz+CGphAkhZpqEMDE95sR2V3D8cuTzdxnnK2rq8Or/uwp++7eVXyfaq/q0AJZdrZYdI53quZEuVSVrWq3ur18ODSvU50uvUs330Z7K4Wn51er1/I3FKlslgTEhrGbh0VeP1r9NXROcOJWwGTBqNLtPpiucpLW6OBbBrE69MCaEJTJ5fE47NT6XVfWKprIMxoyhq4nSEJYue61SlSph5j+2WDpXsXl+Qa2XA4NxYukcI8ah12aAM3dHwsQh7MpVTdhtGs0y/kEIMcMkhInpMUNYy5lqR6E5RDWfgxfvhhWvUeHp2f+BkQOVD5LOJNRYB78xlmH5qwFjd+RZbzdef11xVETdUrWr0V1VrG6NdqplxJr28vCkaXDjt+F9D08+EsJbAzanCpIjHcVzIY/GokuMTQALVHXvFHDPlsOs+8yDnHnHgzy4o3fc/YeGEmz4p9/zpz2DzDf6rgB8Ljsep42XOlXPXVc4SSqbV5Uwt4Man4toKkcmV7CWIqG8J8zcyWg20pfyOG24HTbmVXutJcVFdcUevEpjJNrr/DzbMcyZdzzAn/aqpXSzymZWwszXrqS93s8jH7uUa9e0VLxfCCGOloQwMT1mP1jzmeqj2RfWuVntcFx9g6oGmdP0Y+N/gZMcVh/NgaMLLlCVtZZ1aicjGCFsnfq8dglc9nfwjl+oHixQk/InCk8uf+W5XqU0TTXRdz6nesuOJYTZbHDD/8B1/3H0rzHH7tl8mA/f/YL19c6eKG6H+s/DKz3jjyF6eFc/w/EMf3Plcj7+6hXW7ZqmUed3kzTOYtR11Y8Vz+TwGRPsQU3C32805Tvt2pQrYZqm8YP3nMv/e9Uia8zEypbi1PlKS4Z/c9Vybrt2JQUd/viKGtBbsRLmmHhmV1utD/skZ0EKIcTRkE5TMT2JkkoYqBBWtwT2PKCWIpdcrpbmNn/TqDQZ0+RLq1UJI4SZA0odLrj+62pExbwNahL96htUH1gmpsY32OxQ1VJs+B89pEJY+6uO/mdZeAE8/Q11fFJpX9nRaL/w2J4/xx7a2c/vdvRy+5+dQV3AzWAsTVOVh0QmR2+FCfjPdgwzL+Thw1eOH8dRF3DRFU5auwr3D8ZJZPJ4nXaWN6nAtKs3yoHBOJoGq1qqGI5neHhnPzu6R8nkCmga43q+TBvby4/sWdlcxf0vqbBfqXm+rdbHn1+8mK89tJfNB4bLnut12tE09VfUfZSDU4UQ4mhJJUxMj1UJW6s+mk3wux9US4Xealh2Fax5k5p7lU+rWVylzEqYr2TO0RnXqVBks6nJ9lXz1Gu96m9UADO5/KqC1rNNBbRjqWAtOL94fuWxvM4poMcIWlsOqgrmYCxNfcBFU5WH3tHyY390XefZA8MTzqkyA445EuLAYNxqzF/c4MemqXMmOwbjzAt51diKeJYfPnuIr/5xL32RNDXG0NTJmN+nvd6Py6jaTdQ8r2kay5oCRI2hsDVWP5lm9ZmZlT8hhDhe5L86YnriQ6o3q26pGrbavVUtDfbvUE3xAJ6QOui6dYP6OtpX/hoJY87YZOcfTqZ1A+y8X31+rCHMdJqHsL5RI4R1qICsQpib5ioPvZHyg8A7hhIMRNPWIdJj1flVL9cZ86qoD7g4MBAnkcnhcznwOO201/nZ3Rfj+UNhVrUEqfW7GE5kODAYJ5Mv8PyhEeqmcDbj6nlVrJ5XxYYF1dQYZz0GJxkjYVbhHDaNYMlkeZ8xpuJojxASQoijJSFMTE98QC0jOr1qKXDPA/DyL9V9y68pf6x5BuLYvrCxy5HTddVnip8fyzKivw7qjX4ms9fsNJTLFxgw+rA2d6hK2EA0TUPQTXNofCXMXNI7d6IQZjS9t9X6WFTv58CQqoR5jbCzrCnAE3sHOTSc4JIVjWrHZDzDoSHVqL+nP1Y2nmIijVUefvNXFzG/xmctXU52vuMyI4TV+F1lU/XNAbATNeYLIcRskf/qiOmJDxTPOVx+jTr+54mvwvxzxh/XUzpNvlAo3m427Xtrju4amlarJUun/9grWMuuUpP23YFje52T2GAsQ76gU+d3sb1rlEgqy0gia1XCRhJZUtni2Y9bO8OEvE6WNFR+z8wq1gIjhO3rj5HI5q2K0/KmoLUsePnKRmr9LnIF3Tp7Ur3G+J2Rk6k2KmGTzfJa3qSut3ZMr5lUwoQQc0VCmJiexHDxjMdlr1Yf4/1w9rvGP9Y8V3FgF3x5Jdz3YTVkNTGsljQdR652TOjKT8OHt6lhqcfiitvhvQ8f22uc5MzG+2vXNpMr6Dy8U+0grDcqYQD9JUuSA9E0LSFPWTWpVFOVes6ieh9ntFQxFM8QTmStcRBmRWplc5DWam/FBvypVMJKTaUSttyqhJU/xuwJ80hPmBDiOJPdkWJ60qPgNoao1i5Sg1QjPWo341juoKpWvfxLdYzQc9+BXEZN1D/aKpjJZoNAw7G9BoDDrf6cxszlxitWNvH9pw/xxF61A7Yh4LKW6npGkywwxksMGf1iE7l2bTMh7yaWNgYJl0zHL1bCVEXqMmMSfekoinXzQ2zrHK04nmIy1WYIqzCs1dQYdBPyOse9tjmFXyphQojjTf7XT0xPOla+dPeaL8AN35j40OpgEwzvV+MqVr1OnQWZHC7fGSlmTTSV5aP3bLNmb1XSazTlr50fosrj4Im9auOEuRwJlI2pGIpnJg1JbofdClirWqqs6SRmCFvRFORTr1nJrRe2A8WdigG3g/OXqCpr3SQhrxJrOXKSSpimaXz+DWv584uXlN1uVcIkhAkhjjOphInpycTAVRLCFl86+eODLSqEzVsP8zfBK/fB8IHiodtiVj13cISfPd/JOYtquGlT5c0HPZEULruNOr+L5U1Ba0xFQ8nRQGZQAxiOZaa8XOh3O1jSEGBvf8xqzNc0jfeVBCGzR2tRvZ8Vzerv1lR2R5a6dk0z6WzBCnoTPm7t+Kn35nNkRIUQ4niT/+qcTnIZ2PN7NZnyqJ6fVgdqT6eJPWD0hS04H+qMxv3hfUc/nkKUGU1keXr/0IT3m+Fpe9cEB6mjxlM0hdzGLK3i9Pn6gJugx4nfZbcqYelcnmg6N62QtGZeFcCEAcns0Wqv93Pe4jrOaKlibWtoyq8PcOb8am5/3RkT9qlNprg7UiphQojjS0LY6WTX/fCDN8GeB4/u+Wl1zAyu4OSPK2WOqVhwvuofM8ly5ISy+UJZ5alQ0OkcSVR87PefOcjNdz7N/oFYxfvN8LS9e3TC79cbSVnLjiuMfi2v026Fk6aQh509UXb3Ra0zHqezXLjGCFReZ+XCe8DtoLXay6b2GlpCXu7/8EW01R7jhotpsCphMqJCCHGcyX91Tiejnerjc989uudnjDMEp1MJq10Mdrc6T7JmoeoNg6OfEXYauPvZQ1z55UdJ59RYiEd293Pxvz3M7r7xZzia4ezHmw9XfC0zzL3SEyFnjIDIF/Rxj2kOeYHiDsL6YLHS1V7n56n9Q7z63x9j66EwUPlcx4mcvVBtwmisqhzcNE3j8U9cxjvOO8ajo46SVMKEEHNFQtjpxByauvt3anbXdJmVMPc0KmEb3gUf3KwqX3ZnsRfsWHdHnsIODiWIpXPEUmqWVudIkoIOv3+5b9xjzZD1s+c7yeQK4+83KmGpbIF9A3EODMbZ8E+/56Gd6rWe2jdEVzjJvGpVCTOXI0t3P37xxnX80/Vr1OP3m037Uw9hZy2o4eGPXcpZbdUTPkbTtKNaSpwJ1pywSQ7wFkKI2SAh7HQS7VNN9XoeXvzx9J+fMZcjp1EJc7hUBcxkLknKcuSEhhNqyS9mDDQ1xzz88ZXxIaxnNEXI62QwluGRXf3j7u8dTdFujJbY3jXKZ+7bwWgyy/6BONu7RnnH/z3Dwjo/7zq/HVDhqsbnpKEkhNX6XbzuTNXQbk7Ur53mMNVF9f45C1lHYlbCvEdo6hdCiJkmuyNPJ7FeaDxDHbrd9/L0n380lbCxzKn6shw5oZF45RD2wuEww2PGQ/RFUlyxspGfv9DF/sE4AJFUli89sIvr1s+jN5Li2jUt9Ee7+MIDu6zKWDSV46WuUXIFnf9710bmVavlSE3T+Nwb1tJo9IiZqn0qnO3qVQ3+0x2meiK7+oxmIq/NWmFVCCGOF6mEnU6ifWpuV6it2B82HWZP2HQqYWOZZzX6Z2DQ6ilq2Ahd8bTqCRtNZrFpalNrabUrlc0zksiyuMGP22FjKJZmOJ7hjV9/ku8+dZBvPLKPcCJLa7WH9160mLZaL2/eOB+fy040lSOaUt9nbH/XNWta2LBg/HJxe72fgg4uu63sAOyTXcjn5D0XLT5hK3VCiFOXhLDTSaxXnecYmg+jh9Vv9V2/U6MrpsKqhB1DCFvzBnjTt1RF7hRXKOg8uKO37NzFqTArYXGjEjaazLC8KYjfZefFzuIuxz6jqtVU5aE+4GYonuEPL/expz/G0sYAj+4eAKA55OVvrlrOT/7iAv7tTesIeZ3E0lmiqRw2rTis9EgW1auBvLVjDsAWQghxdCSEnS6ySUiNGpWw+RDpgkNPw49ugsf+TZ3pONqlHpsahfhQ+XOjfUfXEzaWww1r3ginwS/xrZ1h3ve957j5zqcZiE48sX6sSsuRtX4XTVWestcxm/JbQl5q/S6GYhkrmN28qY1sXu2CbB6ztBhwO4xKWI6A24HNNrV/FouNEHYqLUUKIcRckhB2ujB3Q5qVsEIO9v1R3fbEV+Fb18B/nAk92+C718Hdbyk+9/6PwzevmJmesJOIfrRDbQ1mYNp6OMwXHtg5pedkcgWiRvgqVsKyVPucNATd9EeL88PM/q7mkJu6gIvheIb+aJqQ18mrltVbjzMP4TYFPQ5i6RyRVJbgJMf8jLWoXoXv6Z7rKIQQorJZDWGapl2jadouTdP2app2W4X7F2ia9rCmaS9omvaipmmvmc3rOa3FjJ11wRbVEwZqer7TBzYH9G1Xh23fdT30bIXe7Wq5MhmGl36qli8TQ2rml33qv7hPZrf97CU++MPnj/r5ZkWrucpDVzg5peeEE8WlYasSlswS8jpprPLQX6ES1mxVwtL0R1M0Bt0sbwxS5XEY94+phHmcViUs6Jl6b1d7vWpcn+zwbiGEEFM3a921mqbZga8BVwGdwGZN036l63rptry/B+7Rdf0bmqadAdwPtM/WNZ3WzEpYsAk0Yyt+z1Zo3Qiv+TcVxg49Bb/+G7C7IBtXwe2V+yBnBIjh/cfWD3YSKRR0Hny51xpiejTMUROLG/zWDsepPgdUY76u64wmsoS8LnyuAv2RNLquo2kaPaMpAm4HAbfD6gnrj6ZprHJjs2lsbK/l2QPDBMY00QfdDrpGEnictkkPvB6rtCdMCCHEsZvNLU7nAHt1Xd8PoGna3cDrgdIQpgNVxuchoHsWr+f0ZlbCAs2qL8vUuBJaz1af1y9X/V+uANz3VzC0F174nqqUFXLq62PpBzuJ7BuIMZLIUuM7+sAxEs/gcdpoDnk4OFT52KGxzGOBAOKZHMlsnky+QLXPiQYks+rsxiqPk75IiiZjCn2d30U6V+DAYJzLVzQC8DdXLmf/4PjjjMzlSI/TPq5fbDI+l4PPv2EtG9tlxpsQQsyE2VyObAVKz1LpNG4rdQfwdk3TOlFVsA9VeiFN096nadoWTdO2DAwMzMa1nvqivSpM+erAUwUe44DkhlXFx9jscP4HYPGl6uuDT6kesVXXqa9HOk6bfjBzKOl0dzaWGo5nqfW5qPG5GCmpcD13cJgn9w5O8Jzy5cjRpKqghbxOmozA1B9RS5I9oylajEqdWZ0KJ7I0GMFs7fwQr18/9l851ZgfO4rlSICbz1nA0sbTI4gLIcRsm+vG/LcA39F1fT7wGuB7mqaNuyZd1+/UdX2jrusbGxpkvtRRifWBvxFsxttr9oU1rhz/2NB81fu19Qfq6zVvUB/1/GlTCdvSMQxAqsJRQFM1kshQa0ygT2Ty1lmQ//77PXzut8VG/f5Iiu1davSE2Ufmd9mJp3PWMma110ljUIUrszm/9Lih0j6txuDk1a2Ax0E8k2ckkZlWY74QQoiZNZshrAtoK/l6vnFbqXcD9wDouv4U4AHqETNv9DBUzSt+HZqvPpZWwkw2uzp4e+QAODyw9Eows/Fp0hP2rBnCjqkSlqHG56LaV6xSgZpobw5KBfjn+1/hXd961niOun1+jY9YqhjCQj6ndQD2QDRNNJVlIJqmvUKflhnWJmIGr6OphAkhhJg5sxnCNgPLNE1bpGmaC7gZ+NWYxxwCrgDQNG0VKoTJeuNsGNgFDSuKX9cvh0BTeTArVbdEfWzdCE5vccL9aVAJ64+k6BxJEnQ7SGXzFUdV5As6o8lsxUOzTSMJdcSQ2VdmLklGUzlr56Ou6zyzf5iheIZYOsdIIkOVx2EMVM0xmlTPqfa6aAgWlyPNHrNKs7uOGMJKGvWlEiaEEHNn1kKYrus54IPAA8ArqF2QOzRN+4ymaUaTER8F3qtp2jbgR8At+rEOZxLjJYbVcmRpCLvkE/DehycemmqGsAXnqY+BJvXxNKiEvWQsDZ7dXkNBxxp6Wuqd33qGdZ9+kAs+/xDZfOUgVqyEqaAzYlS5oqkckZQKYZ0jSWveV084aZ0N6XfbiWdKesJ8Tqo8DtwOG/3RlHVOpDm7q67kQO2x5z6OFfCUhjCphAkhxFyZ1f8C67p+P6rhvvS220s+fxm4cDavQaCqYFC+9OgOTB6o6paqjwvPVx+DzdD7IrhO/cb87V0RNA3OaqvhkV0DpHN5XI7y/1/Z0R3BZbcxGEszEs+MCz7ZfIFoKketvxjCzKpWLK0qaOlcni0Hh63ndIWTjCQy1PhdBDxOOoYSZT1hmqbRZMwKOzCgQthC49Bpr8uOz2UnkclPYTlSQpgQQpwI5L/Ap4MBowm8tBJ2JKuuU0cVLbpEfW1Vwk6DENY9yuJ6P7V+FZ5S2QKlve7pXJ5wIsvK5iA7e6OMJLLjQpi59FhTthyZJZsvkMqqylkslWNzxwh2m0a+oNMdTjEcz9AS8hBw24mlc4STWZx2DZ9LzXZrDLrpi6SwaRqt1V48Trv1PesCLmzxLP4jHK5dOjdsOnPChBBCzKy53h0pjoeBnWoafqjtyI81eavhko8Xp+MHm9XH02A5ckfXKGtaQ7iNgDO2Od88jmhFswqkpeMnTObSozmiwnxczFiGBDWCYvOBYc5fXIfdpnF4JEHHYJzWai9+l4O4MaIi5C0emN1Y5aY/mmb/YNyaYG+q9buPWAUDqYQJIcSJQkLY6WBgJzQsL46nOBpmJewUb8wfiqXpHk2xtjVkVZnM0RKmPmNO1/ImFcLCiSyHhhLs7S8ORh2Kq8fU+l14XXbcDhvhRNZqyAeIJHPsG4ixri1Ec5WHJ/cNEc/kWd0awu92kMjkGYlnCHmLQWlVcxX7B+K80h2xJtibLl3ewBWrGo/4M5Y240tjvhBCzB0JYaeSbAriFYaA9u+sPIpiOqxK2Km9HLm9OwLA6nkhPEYfmLl8aBow5nStsEJYhn+4dzvvvWuLtZPSqoQZoyNqfC5G4hkiJaMpOkcSFHTVVN9a7WXb4TAAa+aFrCXD/QNx6kpmgN36qkU0Bt1k8gWrKd/0N1ct5+9ee8YRf8ay5UivVMKEEGKuSAg7lTzyOfjfy8pvSwxDrHd6/WCVmEuZvhPryJp8YWY30z57YAibBmfMq7IqYWOXI81DtM1K2EgiS1c4yYHBuLVrcdjqCVOVpmqfk5FEtmw58tCwGjNRF3BZQ1ddDhvLmgJWX9euvihntFRZzwm4HXzqNSpQr2w+ukDsc9mxGZtipRImhBBzR0LYqaTrOQgfgnzJYdGHnlYf5286tteetx7e+StYfPmxvc4M+t32XtZ/+kEe3NE7I6+Xyxf42XNdXLK8gZDXWRLCyith/ZE0Ng1aa7y4HDbCiQz9xpiJh3f2A8XJ92Y/WLXPSTiRIVoSwg4aIazG52JetTp+aFVzEKfdht9dbLhf0xoq+/7Xn9XKg39zMRcsqTuqn1PTNAJuBzZNTeYXQggxNySEnUr6jbPRE0PF2w49CXZX8ZDuY7H4kmPrK5tBj+4e4P0/eI5oOsc+Y1zDTLxmbyTFTZsWAOBxmsuRYythKeoDbuw2jRqfk95Iypr79cdXVAgbiKYJuh047eo1zPMjS3vCDhkDV2v9xRC22ghcpUuGa1qLlTDT8qag1ax/NIIeJwG345heQwghxLE5MX6jiqMXPgz3fgCGDxTDV2lf2MEnjan3kw/wPNls6RhGAxw2rewIoKnoi6T41C9essJVJJXl/d9/jtvv3UF9wG01txcb88dUwqJp6wihGp+L3X2qIb+pys3mjmGGYml+/3IfZ7fXWM+p9rkYTWaJpscvR9b4XbQaIWzNPBXCzOVIt8PG0oaZ3wwRcDtkKVIIIeaYhLCT3VP/BS98Hx7/9+JtcePkp3QMerYVB66eQkYSGUJeJ0GPo2yJbyqe2DvID585ZDXCP7FnkN9u7yXkdfK3V6+wqlcexwQ9YZG0dUh2tc/JvgEVwm69cBF5XefW72ymN5Li5k3FkSA1PifhRJaIMQFf09RwVlBjLDYsrOG1Z7ZwpREAzUrYqpYqHPaZ/9c06HHIeAohhJhj8l/hk1k2BdvuVp9v+1HxdrMi1rkZCjlYeMGsXULnSIJMrsDiWajWTGYkkaXG5yJX0KddCUtkVKjqGIpz7uI6tnePYrdp/PwvLygbfuo2lyNz4xvzz5yvKlY1Ppd1fuRFy+o5OLSAHz17iPqAiytWNVnPqQu4yRV0Dg8ncNpVT9ZIIovHacPrsuPFztfeusF6vFkJq7QUORNetazeeh+EEELMDamEncxeuQ9SYahdAvmM6v0CVQkb2AX3fVjN9Zp/zqxdwj/9+mU+9pNtM/qavaMpCkfY9RhOZKj2HV0lLGmED3Mn4/auCMsaA2UBDEorYcXlyFy+wFA8bU3Ir/aVHpzt4eNXr6A+4OJt5y60KmoAC2rVYNWXeyJlS4G1Jc8v1RB0E3A7eNXShmn9bFP111cut3ZZCiGEmBsSwk4Wlc41f/mXanTE5X+vvm5ZD5pdhbBf/Dlk4vDOe8EzO9UUgNFk1jpkeiZEUlku+cLD/OS5w5M+LmxUwo4mhJkVoAMDcXRdZ7sxIX8sd4XG/KF4Bl3Hmkxvngtpt2nU+V3U+l08/onL+fAVy8peq61W9Xzt6o0SKFkKrPFXDmEBt4MXbr+Ka9Y0T+tnE0IIcfKQEHYyCB+CLyyFR/61PIyNdqr5X0uvAJsTmteArw5i/dD3Mqx/C8zfOKuXls4Vxo1wOBb9kRTpXIFnD4xM+rhwIkvI5yTocZYNQJ2KRFaFtgODcfoiaYbiGdZWCmEOG5oG6ZIQ1juqRlGYIazGCGENATc2Y/iWx2m3Pje11ahKWDpXIOh2Wj1ftROEMKCskiaEEOLUIz1hJ4OOJyAxCI/8C6QjcPU/q9tjfdC0BjwheMcvoG6JmgvWsxXyaahbOuuXlsoWxjWuH4thY9L8ju7RSR83kshYM7iOdjny4HCCbZ1hoHLvlaZpuB02UiW7I80mfPPIIHM50twtORG/20F9wMVgLFNWCZsshAkhhDi1yf9qnwx6toLTB2e9A576mtrxWMirilfQaP5edBFUzQN/PfTtULcdhxCWzuZnOISpafR7+mMTvm46lyeRyVPjc1LlcU67MT+eVq+byRX43fZebJrahViJx2kvq4Tt7ovhtGu0GyHMDIJTOTh7vlENC5b0hNVM0BMmhBDi1Cch7GTQsw2a18Kr/0kdG/TbT6hZYHoegi3lj/XVg25UbmqXzPqlpXMFUrmCdWbisTIrYfmCzis9kYqPCSfUY6p9Lqo8DmLp3BEb+Usls8XK2S+3dnHe4jp8rspFYY/DXrbcuqcvyuL6QMkQVmM5MnjkOWxmc37Q45jScqQQQohTm4SwE12hAD0vQss68NbAhR+GQ09B9wvq/kBT+eP9xm46p7946PYsSmXz5As62fzMhLAR48xFKB6mPdFjVGO+k4IO8czUlyQTmTxNxvKhBvz9JIdee5y2shEVu/ujLGsqjuOonkYlzGzOn0pjvhBCiFOfhLAT3fA+yMZVCIPix4OPq49jg5a/Xn2sW6wmgs4yc5r82FlaR2s4nsHnslPtc7Kjq3JfmFkJqzFGVEDlvrA+4zzHsRKZPAvr/MwLebj1wkWcMW/i3aMep91aFk1kchweTloHdwM0hzwEPQ5WT/IapmIlzEnA7AmT5UghhDhtSWP+ia57q/rYsl59NJcYDz6pPo6rhJkhbPb7waA4viGVyVM1A8fgjMQz1PpdLKj18UpvtOJjwkYlLORzEkyo7zk2hO3tj3Lllx/jZ+8/n7MX1pbdl8zkqQ+4eOhjl+J2TP7/IW6HzVqO3GMcT7S8pBIWcDt44R+uwm47cuBtM0JYwO0g6DYrYXJ0kBBCnK6kEnai631RDWFtWKG+rmoFh6cYzsaGMN/xC2G5fIGc0Ys1U2MqhhMqhDWHPAxGVZP+lV9+lO8+2WE9ZsSqhLlKKmHlzfnmod6Hh5Pjvkcik8PncuBx2o94gLW7pBK2u0+FwmUllTAAh902pYOw2+tUM3+t32UtQ05lGVMIIcSpSSphJ7rRw2ogq92omNhsqhrWvwM81eMP5jZ7wo5TU74pOUM7JEfiavREnd/FUDxNMpNnb3/MCkAwties8nJkvxHgKg2STWbyeF32cbdX4nHardfY2RvFZbex0KhoTde8ai/ff/e5bFhYjd2m8T/vOJuljcEjP1EIIcQpSSphJ7pIz/gdkHWL1cext4MaznrpJ2Hla2f90kpHSMzUmAqzElYXcJPKFjg0nAAgni6GrHAii9uhzlw0Rz2MHdg6YPSDRSqEsHgmj2+qIcxhI21sPrj/pR7OW1J3TAdqv2pZPT6XA7fDztWrZRq+EEKcziSEneii3VA1NoQZS43BpvGPtzvh0ttm9agiU2klbKZC2EhcHUdkjm7Y2at2SMZKQphZLQOoOg6VsHSuwGO7B+gZTfGWTW3T/ImEEEKIyiSEnch0HaK9FSphRggLzG0lpTR4zcRyZDqXJ5bOUet3Uh9QIctchiwNWeFk1jqz0ayEjQ1h5s7IsSEsly+QyRfwTzAXbCyP00Yqm+fuzYeoD7i4YlWF4CuEEEIcBQlhJ7LEMOQzahJ+KbPfq1Il7Dgqbcaficb8EWNQa43fRZ1fNazvMnZIls4BC5ccWeRx2nDYtHGN+RNVwhJGWJzycqTTTjSV46Gd/Vy3rhXXEXZTCiGEEFMlv1FOZNFu9XFsJax+uTqwu3bx8b+mEunczPaEDcdVw31t2XKkCmExo9L1+J5BdnRHaA6pDQmaphH0OCZcjhzbK2aeGzmd5chYOkc2r7OpveZofiwhhBCiItkdeSKL9KiPYyth/jr4y6ehesHxv6YS5ZWwYw9h1q5Hv4s6Yzmyc0SNmIil83SOJLjl28+ypCHAx65eYT0vaJwfqes6V3/lMd68sY2hmFkJKw9nicw0K2Ella81raGj/MmEEEKI8aQSdiKbqBIGUL8UHHM7bT01W5Uwvwufy4HXWQxKsXSW/QNxcgWdz7x+Na3VXus+sxLWG0mxuy/GD589hHmU5NjdkeYuS69zav//4TauocrjYH6N9wiPFkIIIaZOQtiJLNqrPh6HMyCPRjpbOidsBnrCSuZ/AVY1DFTVbdCobo099DrocRBJZdltTLTfbwxqba32jusJS06zJ8ycqL+mNTSlgaxCCCHEVEkIO5FFutXwVfuJebTNdHvCth4O89S+oQnv74uksNs0aoydj3Vjwpa5NFk95rzF+TU+9g3E2T3mmKNlTQFi6Ry5fDEgmsuRfvfUe8JAliKFEELMPAlhx0s2CQefmt5zohUGtZboCic5MBg/xgs7eulp9oR95Q+7+fR9Oya8vzucornKYw1DrQuoHZIB45zFzhE1uNUcT2Ha1F7DcDzDAzt6yypcSxvUGY+lTfvJzPSWI80QNpUDuoUQQojpkBB2PET74DuvhW9fAz3birdHetQssIlEesY35Zf459+8zF/f/cIMXuj0TLcnLJbKWX1flXSFk8yrLh7DZC47LqxTxwR1jiQJuh04x0ys39iuDujecnCEM+eHWFSvzmhc2qhCWOmS5HQb8xfU+nA7bNb3EEIIIWaKhLDj4bcfL4avgV3q4+4H4csr4efvg2xq/HN0HSKdk1bCwomsNYphLpjBK+h2TGlYazyTZySRQZ8geHaHk2UN92ZPWLsRqg6PJAj5xi/NLq73W0uXy5uCXLCkjpaQh3qjknYsIeycRbW8eMery65LCCGEmAkSwo6Hw8/CqteBZoOhvZBLw+8+Ad5aeOkeuOv1EB8sf07vi5Ac4YnUQt7w9Scqvmwqm694LM9M+9/H9vPar/5pXHgylyNDPqc1ruJbjx/gxv9+suLrJDNq3lbpEUSmfEGndzTFvNIQZgSrRXUqhHWHU1bTfilN09hozPBa1hTktmtX8pO/ON8KbN97+iAXfv4hUtk8CXM5coohDMDtmPpjhRBCiKmSEDbbon2qt2v+OWqu19Be2Px/MLwf3vhNuPE70LMVvnUN5EsC1Su/Bs3GI5zN1sNhCoXx1aNUtkAiky9rkJ8NT+0fYkd3hJd7IuXfP5fHbtMIuB1WVeylrlF2dEcqvQxxowplTsYv1R9NkSvoY0KYqmSZlbB8QR/XD2baZCwXLm8MEPQ4mV/jI+RVj71vWzdd4SQ7e6MllTAZkSeEEGJuTek3kaZpPwf+D/itruvHPovgdGIuQ7asU8cNDe1VVa+mtbD0CnVffBDu/xhEuqCmXd228zew4HyG9RAFPUo0lRu3FGf2ZI0mszQGZ69a02E0/z/0Sj+r5xV3CaazBdwOGx6n3VqOHE1mSWbz6Lo+bqSDOa1+OJFhgdHnZeoOq52Ppct+V65q4q+vXMa5i4r9WJUqYQBv3DCfSCrHhoXFqfZVxrmS5kHj27tGSWbyuB027DYZNyGEEGJuTbUS9nXgrcAeTdM+r2naiiM9QRjMENa8Vh28PbQPOrfAwguKj6ldpD6aE/KH90P/Dlj5WqvKZc7QKmUuB5oDSfcPxHhwR+8xXe7Pn++0QhdANl/g0LDalfjQrv6yx6ZyeTxOO16n3bqWcCKDrheDj0nXdev8x5GS5vxcvsC3nzjAHmPGV2vJQNSQz8lfX7mcKm8xfNZMUAmr8bv4yFXLy5r2Q97yx+7oHiWRyU+5H0wIIYSYTVMKYbqu/0HX9bcBG4AO4A+apj2padqtmqadmEOsThQ9W1UFzFMFdUsgE4NsHBaeX3xMVav6GOlSH/c9pD4uv8YKM+EKvV/mEmA4oe779hMdfPSebeMeN1WHhhJ85J5tvP5rT/DsgWFA7UjMFXQW1vnYejhsHQekvn8Bj8OGx2mzKmHmdZpVr9LHmi1lQyUhbOvhMJ++72X+/Q+7AWgJeRjLHFEB42eETcbjtOEyQtm8kIeXuswQJkuRQggh5t6Ue8I0TasDbgHeA7wA/AcqlP1+Vq7sVNHzIsxbrz6vW1K8ve284ufmDsioUQk79DQEmqF2sRXCKlbCzIBmhLDRZJbomOGkUxFJZUlkcuzqU8NOC7rO7fduB+DAoKpQ3XJBO7oOj+8tbiBI5wq4nXY8TrsVCEeNa0mM2S1pNsRDeSXMnILfF0lT5XEQ9IzP9HabZlWvJuoJq0TTNKq8Tqp9Tv5s3Tx29UYZTWan1ZQvhBBCzJYphTBN034B/AnwAa/Tdf06Xdd/rOv6h4DAbF7gSUvX4amvw+ghaD1b3Va3VH2saYeqktETnhA4/WpCPqgQtvB80DTSVrVrfAhLlfRhAdauw0hq/O7Dydz67c188ucvsdsIYTec1cre/hiZXIEDg2op8rVntuB32dnSMVL2/d0OG16nnVRO9YFNVAlLlHw9XPKzlFbF5k0yBsJvVMMm6gmbSEvIw/mL61jbGiKb13ly3+C4Y4+EEEKIuTDVdZmv6rr+cKU7dF3fOIPXc+p4+V544JOw8s/g7FvVbaE2cHhgwQXlj9U0Fcoi3RA+DKOH4YIPAcVq19gdhbl8gZyxY9IMPtFUsSI2naCxpy9qTd6fF/KwYUENdz11kI6hOAcGY1R5HDQE3GxYWMPmjmF+/nwnX3xgFwvqfLiddtxOO8lMgXgmT964pslCWGklbCimPg96HMyvKW/WLxV0OxiIpqdVCQO4851n43XaraAa8jq543Wrp/UaQgghxGyYagg7Q9O0F3RdDwNomlYDvEXX9a/P2pWd7AZ2qo83fhfsxttss8Nb7ylfljRVzVPLkYeeVl8vUMuVxSXH8kpYqqTxfdS4zzyeZzqzwxKZnFU5e3hnP2ctqGFZkypu7jbC2aKGAJqmsam9ln//w26++sc9dI+myOQLLGkIGI35+bJrHDu8NV6yHFk6NX84niHocfCdW88Z10hfKuA52kqYqq5V+1x8+5ZNrG6tojE4vu9MCCGEON6m2hP2XjOAAei6PgK8d1au6FSRHAF3qBjATIsvgdD88Y8PzlOVsENPgSsITWsASnZHlger0mOCwmOXI6cRwnpHi9P6I6kcK5qDLGkIYNNgd1+MAwNxFhtzuja216Dr0DGkligHYxk8TrvVmB8uucaxISyRVl877VpZf9tgLE19wM3ZC2usY4Yq8buOLoSVumxlowQwIYQQJ4yphjC7VjL0SdM0OyCNNZNJhsEbOuLDLFUtqhK2749qfIVNNY9nJtgdWRrCRq3lyOlXwnoj5UcmLWsM4HHaWVjn59cvdtM9mmJ9WzUAZ7XV4LBpOO3FGVvmnLBcQS/r70pmyvvSzMb81movw/EM923r5sBgnOF4ZkpLp2YlrNovm3GFEEKcGqYawn4H/FjTtCs0TbsC+JFxm5hIKgye6qk/vqoVCjkY6YDlr7ZunnA5Mltcjgwnsuh68TigaYUwoxK23FiCXN4UBFQY2z8Qx+Ww8fr16hBxr8vO69bN4z0XLcbjVH91zDlhAH0lgc6shCUyOZ4/NGL1hLXWeDk8kuRDP3qB//3TfoZiGet4oskE3Q7sNo2gW8ZLCCGEODVMNYR9AngYeL/x54/A387WRZ0SkiPgrTny4wxxd0Pxi2VXW5+auyPHjqgYuxyZyhaspvjRZJZv/mk/v9rWfcTva1bC3nLOAkJep9UPtqJZhbFr1zSXzeb695vW84lrVtJmNNF7nDYrkJUubZqh638fO8CN//0U/VF13/xqn1XdOzAQZyiesQ7qnsyiej9Ljd40IYQQ4lQwpbKCcVTRN4w/YiqSYWhcOeWHD2j1+IE9LKA92Iq56DbR7kjz9iqPg0gya+2MBDX361dbu1nSEOC6dfMm/b59oymqPA5uuaCdt5yzAI9R1TKPJ3rLOQsqPq+t1see/hhuh916TunSprk78pkDQ+QLutVHVjoRf99AjJFExjojcjIfuGwpf35JhQ0NQgghxElqqnPClmma9lNN017WNG2/+We2L+6kNs3lyD7qAfh9bj1/fEUdD6Tr+oTLkWaFrDnkIZzIEE2XD0Pti6QYiKY5kp7RFM0hD5qmWWEK4NVnNHH/X13EeYvrKj5vQW2xEmYOP+0cSeIwzmRMZfNk8wVeOBQG1DR+gIXGmZHr5ofoj6bJF/Qp9YTZbBouh5w3L4QQ4tQx1d9q30ZVwXLAZcBdwPdn66JOero+7eXInnyQ92X+hm/xen6y5TAAGWPyvddpJ57JW8t4UDy8u6nKw2gyW7Yjck9/jIIOA7Ejh7C+SIrm0PghqTabxhnzqiZ83nyjouVx2q0+si0dw9T6XThsGolMnh3dEas37OCw6i+7enUzX33LWbznosXWa01lOVIIIYQ41Uw1hHl1Xf8joOm6flDX9TuA187eZZ3ksknIZ8BbPeWnDMbSPFjYxOrFC9g3oI4KMqtgzcZ5iuFksRpmNuY3V3ko6KqiBWru665eNfl+OJ4hncvz6ft28EpPpOL37Y2kaK468nLgWG1GJcztsLG0IUC1z0kik6fa58TrtJPM5tnSMWw9vjucwu9SS5fXrZvH4ga/dV99YPrfXwghhDjZTTWEpTVNswF7NE37oKZpNyDHFU0sFVYfp7EcORBL47LbaK/zWcNM00bQajJCUukcLrMxv6lKBbSukSSgQlnpdPoXDoX59hMdfOwn26zGfVMuX2Agmqa5avqzs4rLkXZsNo2NC1XVL+R14nXZSWbyPHtgmPk1Xmwa5At62cHZ7XXFECbHCAkhhDgdTTWEfRh1buRfAWcDbwfedaQnaZp2jaZpuzRN26tp2m0V7v93TdO2Gn92a5oWnsa1z67elyAxfOTHAfRuL39s0jhfcRqVsIFomvqAi1q/m0gqRzZfsAa1miGp9LgfsxJmnre43zhoe+z5i88dVNeyozvC3ZsPlX/PWJqCTsXlyCNZVO9nTWuVtWS5sb0WgJDXpUJYNs/uvijr2qqpMypdvpKDs/1uh/VzyXKkEEKI09ERQ5gxmPUmXddjuq536rp+q67rb9R1/ekpPO9rwLXAGcBbNE07o/Qxuq7/ja7r63VdXw/8J/Dzo/1BZtxdr4cH//7Ij8vn4FvXwONfLt6WDKuP0+gJG4xlqA+6qTWGkY4kMtZypFntGqlQCTNHSrzco5Yg59eUB6rnjRDWVuvlh8+Uh7BHdw0A0F4/8ZmNE/E47fz6QxdxwRK1oWCTEcKs5chMnqFYhoaA21pu9I2Z8bXImMR/LFPwhRBCiJPVEUOYrut54FVH8drnAHt1Xd+v63oGuBt4/SSPfwtqCOzcy+cgMQS7H4BCYfLHDu6CTBQiPcXbzErYNJYjB6Pq+J4aY2luJJ61liPNEFM6hsJszF/aoELYrl7V82VWwvxG1en5QyM47RrntNdZh2WDmiX2hQd2sam9hvMn2AE5HWtbQwTdDlpCHrwuO+Fklmg6R33ARb1R6fKV7L4EWNkSpCXkwWmXXY9CCCFOP1MdP/6Cpmm/An4CxM0bdV2frHLVChwu+boTOLfSAzVNWwgsAh6a4P73Ae8DWLCg8tyqGZU2mtgTg9D9PMzfOPFje7YZjx0q3mb2hE2zMX9ta4haoyo0HM9YQ1Drg+o281giKPaLhbxOGoNu+qNpPE6bNX1+dWuIZw8MM5LIsqjeT33QxXAig67raJrGd57oYDiR4a7rzpmRAaguh41f/9WrqAu4ee7gCAeNkRS1fjcNRoj0u8tD2EeuWs7/u3DRMX9vIYQQ4mQ01RKEBxgCLgdeZ/z5sxm8jpuBnxpVt3F0Xb9T1/WNuq5vbGhoqPSQmZUaLX6++winM1UKYdNcjiwY5y7WB13FSljJcqQ5zLQ0hKVyeVwOGzabZu1UDLidVHnVcmZ7nY+Q8fn8Gi+1PheZXMFq2j84HGdeyGsNZZ0JC+v8BNwOfC67Nbi1LuCiPqiu3+sqz/xBj9O6diGEEOJ0M9WJ+bcexWt3AW0lX883bqvkZuADR/E9ZocZwjS7CmGXT9Ib1r1VfRzbmK/ZwBWc0rcbSWTIF3TqA25rp+BwPIPf6KHyux14nXaiqSz7BmL8/PlOUpk8HmN46YJaH88dHKHK47CCV3PIS0PQzWgyy4JanxXuzNeNJHNWYJtpHqfd2olZuhzpd9kne5oQQghxWplSCNM07duAPvZ2Xdf/3yRP2wws0zRtESp83Qy8tcJrrwRqgKemci3Hhbkcufwa2PUb6H8FGleNf1whr3ZRwvjlSE8IbFMrNA4avVr1ATfVPqMxP56h0agguR02gh4H0VSO377Uw9ce3sflKxutCfdtRjN+wOOgyqOePy/koTHoZm9/jLZaX9kyZ1utj0gyS8g7O4dhe0t6v2r9bhqC5u5IOXxbCCGEME11OfLXwG+MP38EqoDYZE/QdT0HfBB4AHgFuEfX9R2apn1G07TrSh56M3C3ruvjQt6cMSth57wXbE547rvl93c9B5kEDO2DbBzql0MuqW4DtRw5naZ8Y7J9fcCN22En4HYwXLIc6XHaqPI6iaaz1g7JQ8MJ3EbPWHE50sGypgCL6/2cvbDGCj8Lan3UGtWoYeP4o9Fk1qqazbTSURR1AVdxd6RUwoQQQgjLVJcjf1b6taZpPwIen8Lz7gfuH3Pb7WO+vmMq13BcmSGsbgms+jN48W648g5wetSy4zevgmv/FQKN6nGLLoHB3aqR37Vg2kcWmSHMDE01ficj8Yx1TJHLbrcqYR6nClGHhxPWwFQzhAU9DuoDbh762KXq9QLFEBYwljbNWWOjyaxVNZtpHiNsOe0aQbejZESFhDAhhBDCdLSzAZYBjTN5IScUM4R5QrD+7SpUHXhM3TbSAXoeRjshpg7apmm1+mguSabC09wZqYKRubOx1udiOJG1KmFup42gx0kklbOm5qdzBWs5ckFJY36pBXU+XHYbbWN6wgAiqVmshDkdxs/jRtM0WkIeHDZNjicSQgghSky1JyxKeU9YL/CJWbmiE0FqFNBUY73ZCxYx9hSMdqqP8UFwGoNR65epj2YIiw9AbfGA6iN+O2PwqlkpqvW7GIilrYn5Zk9Y50gCW8k0CXOERVOVB7fDZvWTmd68sY0LltQR8jrRdR27TWMkkSGbV7skZyuEeV026+cAqPa5+O2HL2JhyVFFQgghxOluqsuRU9vmd6pIjYKnSjXW+42RGGbVywxhiUG1POmtgUCzcdsw6DpEeyHYMvVvl82jaeAyhpbW+F3s7osVK2EOO1XGcmRpFDYrYXabxrdu2WRNoC+9f2mj+kenaRo1PhfD8SyjSVVNC/lmKYQZ11V6HNGyptPrr5AQQghxJFNajtQ07QZN00IlX1drmnb9rF3VXEuNgtv4cR0u1WQfHxPC4gOqGuZvAJ86sofEkFq6zGemHcLcDps1NLXW52I4nrEGsrocxnJkMstIojj13u0o9lhduLR+3LmRY9UavWZmCJutnjBzHlidHMwthBBCTGiqPWH/qOu6NcFU1/Uw8I+zckUngtSo6gczBZpKKmHGIQDxQfXHV69CmmZTISxqHF8UbJ7ytyvt7wJVCUtm84STGZx2DbtNNbincwXCyeLRReZy5FTV+NTU/IhZCZut5UirEiY9YEIIIcREpvpbvNLjTt2hT+NCWKOqfEF5T1hiEPz1atnSWzsmhE2/EmYye6n6Iimr2hX0qLe7dJBHaSVsKmr9rvJK2CyPqKiVSpgQQggxoamGsC2apn1Z07Qlxp8vA8/N5oXNqVSkPIT5G8ZXwrJxCB9WIQzAV2eEsF719TFUwszw0jWStMJZsGTpcF7IAxxFJcyvljlHZ7kSZv4s9QEJYUIIIcREpvpb/ENABvgxcDeQ4kQ6ZmimTVQJyybVx2rjEPFsvNi476tTjflHsRyZyubxlFS1Wo3ergOD8ZIQViw8Lm9WTe6lwW0qan0uRhIZa8xF1SxNzDfDV2u1nAsphBBCTGSquyPjwG2zfC0njkohLB1RE/IBWtZD+JD63GdWwmpheL+qhHmqi+MrpvLtsgVr+j1gNdhHUjmrr6q0Era8KcgjuwaOqhJW0KFzRE32n61K2LKmIL/5q1dxRkvVrLy+EEIIcSqY6u7I32uaVl3ydY2maQ/M2lXNpUJBBa6y5UhjLm338+rjvLNK7itZjoz1T3s8BUA6V14Jq/E5rYBVsRJmjHvwTLsnTIWuA4NxPE7btHvKpmP1vJC121MIIYQQ4021lFJv7IgEQNf1EU7VifnpCKCrOWEm83iiLjOErS/eZ4awpjWqUf/wM9NaioTxlTBN06xqmBnCSsdJrGg6uuXIpQ3qec8eGJ61KpgQQgghpmaqIaygadoC8wtN09opn6B/6ig9sshk9n3t/p2aot+yfvx9y1+tPsYHjqISVhhXlWq1Qpi63ezfsmmwojnI9evncf6Suml9n1UtQXwuO5FUbtZmhAkhhBBiaqbamf13wOOapj0KaMBFwPtm7armUqUQZlbCoj1wxuvVlHy7G/LpYk9YTTvUr4DBXdOuhKWz+XH9XVYIM243D+AOeZ24HDa+cvNZTJfDbmPDghoe3zsolTAhhBBijk2pEqbr+u+AjcAu4EfAR4HkLF7X3JmsEgaw7GrQNOM2rTgtH2D51erjNCthak5YeSVs7HKkw27D57JT4zu2sQ8b22uA2WvKF0IIIcTUTLUx/z3AH1Hh62PA94A7Zu+y5lA6oj6WhjCHW+14BFh2lfror1MBzFYSnla+Vn2saZ/et8wVxlXC5o1ZjgTVnD/2kO7p2tSuQqOEMCGEEGJuTbUn7MPAJuCgruuXAWcB4dm6qDnVejbc9H2oWVR+e7AF5m0oLk0GmtVxRqUWnAfvewSWXjmtb5nK5sc12c+rVgNZSyfp1/hcx3wU0Pq2ahw2jepjrKgJIYQQ4thMtScspet6StM0NE1z67q+U9O0FbN6ZXMl2AyrXjf+9uu+Ci5/8eurPg2Z+PjHzZt+r5ZqzJ+8JwzgX96wlqD72Aas+t0O/u+WTSxtDBzT6wghhBDi2Ez1N3qnMSfsl8DvNU0bAQ7O1kWdkNrOKf+6cdWMvGwuXyBX0MdVwppDHjStfDlyw4KaGfmelyxvOPKDhBBCCDGrpjox/wbj0zs0TXsYCAG/m7WrOo2kcgWAcZUwt8POu85v55IVEpiEEEKIU9G017Z0XX90Ni7kdJXO5oHKg1fvuG718b4cIYQQQhwn0zt8UMw4sxI23XMghRBCCHFyk9/8c8yshM3mOY5CCCGEOPFICJtjqaxUwoQQQojTkfzmn2OpnFEJm+Zh3EIIIYQ4uUkIm2PpbOXdkUIIIYQ4tclv/jlmVsIq7Y4UQgghxKlLQtgck0qYEEIIcXqS3/xzLC2VMCGEEOK0JCFsjqUmGdYqhBBCiFOXhLA5lp7g2CIhhBBCnNqmfWyRmBmjySwf+tELtNf5AKmECSGEEKcbCWFzZHvXKI/tHmBftRcAj1TChBBCiNOK/OafIz2jKQC6wknsNg2HXf5RCCGEEKcT+c0/R/oiKetzqYIJIYQQpx/57T9HekeLIUyOLBJCCCFOPxLC5kjPqFTChBBCiNOZ/PafI2XLkVIJE0IIIU47EsLmSM9oiqBHbU51SSVMCCGEOO3Ib/85kM0XGIqn2biwBpBKmBBCCHE6khA2B/qjaXQdNi2qBWRavhBCCHE6kt/+c6B3NAnAqpYq/C67VMKEEEKI05CEsDnQO5oGoCXkYe38EK013jm+IiGEEEIcb3Js0RzoNXZGNld5+M6t52DTtDm+IiGEEEIcbxLC5kDvaBK3w0bI60STACaEEEKclmQ5cg4Mx7PUB9wSwIQQQojTmISwORBOZKj2Oef6MoQQQggxhySEzYGRRIYan2uuL0MIIYQQc0hC2BwIJ7JSCRNCCCFOcxLC5sCILEcKIYQQpz0JYcdZoaAzmszKcqQQQghxmpMQdpxFUzkKOlRLCBNCCCFOa7MawjRNu0bTtF2apu3VNO22CR7zZk3TXtY0bYemaT+czes5EYwkMgDUyHKkEEIIcVqbtWGtmqbZga8BVwGdwGZN036l6/rLJY9ZBnwSuFDX9RFN0xpn63pOFMUQJpUwIYQQ4nQ2m5Wwc4C9uq7v13U9A9wNvH7MY94LfE3X9REAXdf7Z/F6TgjhRBZAGvOFEEKI09xshrBW4HDJ153GbaWWA8s1TXtC07SnNU27ptILaZr2Pk3TtmiatmVgYGCWLvf4MCth0hMmhBBCnN7mujHfASwDLgXeAvyvpmnVYx+k6/qduq5v1HV9Y0NDw/G9whk2YlTCpCdMCCGEOL3NZgjrAtpKvp5v3FaqE/iVrutZXdcPALtRoeyUNZrIYNOgyiMhTAghhDidzWYI2wws0zRtkaZpLuBm4FdjHvNLVBUMTdPqUcuT+2fxmubcSCJLyOvEZpPDu4UQQojT2ayFMF3Xc8AHgQeAV4B7dF3foWnaZzRNu8542APAkKZpLwMPAx/XdX1otq7pRCDnRgohhBACZnFEBYCu6/cD94+57faSz3XgI8af04KcGymEEEIImPvG/NOOVMKEEEIIARLCjrtwIktIKmFCCCHEaU9C2HE2HM9QK5UwIYQQ4rQnIew4iqVzJLN5GoLuub4UIYQQQswxCWHHUV8kBUBjlYQwIYQQ4nQnIew46o+kAWgKeub4SoQQQggx1ySEHUf9UamECSGEEEKREHYcDURVJaxBKmFCCCHEaU9C2HHUH03jdtio8szqjFwhhBBCnAQkhB1H/ZEUjVVuNE3OjRRCCCFOdxLCjqP+aJpGWYoUQgghBLN8dqRQvvzgLg4NJ+iPplnWGJjryxFCCCHECUBC2HGw5eAIT+8fwuWwceGSurm+HCGEEEKcAGQ58jiIpLIUdEhlCzRWyXKkEEIIISSEHReRZM76vFGOLBJCCCEEEsKOi0gqa30ulTAhhBBCgISwWafrOtFUjjNaqrBpsLjeP9eXJIQQQogTgDTmz7JEJk++oHP9WfN444ZzqAvIcqQQQgghpBI268ylyCqPUwKYEEIIISwSwmaZ2ZRf5XXO8ZUIIYQQ4kQiIWyWlVbChBBCCCFMEsJmWSSpQlhQDu0WQgghRAkJYbPMqoTJcqQQQgghSkgIO0q5fIFMrnDEx1k9YVIJE0IIIUQJCWFH6fZf7eA9d2054uOiKXM5UiphQgghhCiS8sxROjSU4PBI4oiPi6RyeJ12XA7Ju0IIIYQokmRwlOKZHLFU7oiPiySzVHkl6wohhBCinISwo5RI54mmpxDCUlkZTyGEEEKIcSSEHaVENkcmd+Tm/EgyJ+MphBBCCDGOhLCjlEjnAYgfoRoWSWVlPIUQQgghxpEQdpQSGRXCYkcKYUlZjhRCCCHEeBLCjkK+oJPMqhAWPUJzfiSVk8Z8IYQQQowjIewomAEM1C7Jiei6TlQa84UQQghRgYSwo5AoCV6TjalIZQtk87oMahVCCCHEOBLCjoLZlA9MOqZiJJEBoMYnIUwIIYQQ5SSEHQWzKR8m3x05HDdCmN8169ckhBBCiJOLhLCjMNXlSLMSVishTAghhBBjSAibRL6gVxzGGs9MbTnSqoT5JIQJIYQQopyEsEnc+dh+rv2Px8bdnpxqJSwulTAhhBBCVCYDrCaxtz/GgcE4uq6jaZp1ezw9eU/YJ3/+Is1VXvK6jqZBSCbmCyGEEGIMCWGTGE1mKOhq+THgLr5VCWNOWJXHUXFi/p/2DNIYdHPGvCqqvU7sNm3cY4QQQghxepMQNolwIgtANJXlT7sHCPmcXLCknoQRvJqqPBV7wsKJLLm8TkvIKzsjhRBCCFGRhLBJjCbNEJbjCw/uYmGtjwuW1FuN+Q1B97jlyEyuQCydI57J0R9NUStN+UIIIYSoQBrzJxFOFitho4msNR8smcnhddoJehzjGvPDSdWMr+uwsycqlTAhhBBCVCQhbAK6rjNqLEdGkjnCyWIIi2fy+N12Am7nuJ4wcwkT1PgKqYQJIYQQohIJYRNIZvNk8mpGWM9oinxBt4a0JtI5vC47Abd9XAgzx1KYagMSwoQQQggxnoSwCZj9YACdIwmgeFxRIpPH73IQMHZH6rpuPTZc8jxAKmFCCCGEqEhC2ARKlxUPjySB8hCmKmFO8gWdVLZQ8jxVCTPHiklPmBBCCCEqkRA2gbIQNmxWwnLWR7/LQcBtByCaLj52xHje4no/ALV+GdQqhBBCiPEkhE1gNFns7eo0KmHZvDpLMpHJ43PZCXjUhI/SCfojiQwuu42ljQFAzo0UQgghRGWzGsI0TbtG07Rdmqbt1TTttgr336Jp2oCmaVuNP++ZzeuZDrMnzGnXGIylrduTmTzxTA6fy06d3w1Adzhp3R+OZ6n2OWmt9gFybqQQQgghKpu1EKZpmh34GnAtcAbwFk3Tzqjw0B/rur7e+PPN2bqe6TKXI1urvWW3xzM5kpk8PreDdW3VaBps6RgxesPyjCQy1PhcrGwO4nPZaQi65+LyhRBCCHGCm82J+ecAe3Vd3w+gadrdwOuBl2fxe86YcDKL067RVOWhYyhh3Z7I5Imn8/icdkJeJyuagmw5OMy/PbCT+1/qobnKQ8jn5I1nz+eKVY34XHIogRBCCCHGm83lyFbgcMnXncZtY71R07QXNU37qaZpbbN4PdMymswS8joJesob6+PpHMmsqoQBnLOolucOjvCjZw5xeDjJ9q4INT51aHddQKpgQgghhKhsrhvz7wPadV0/E/g98N1KD9I07X2apm3RNG3LwMDAcbmw0YQKYVWe8krWUFz1h/lcamfkxvZaEpk8EeP4omQ2L834QgghhDii2QxhXUBpZWu+cZtF1/UhXdfNrvdvAmdXeiFd1+/UdX2jrusbGxoaZuVixwonM1T7XASNEOZyqLeqZzQFQJVRIdvUXgPAwjof9Ublq1pCmBBCCCGOYDZD2GZgmaZpizRNcwE3A78qfYCmaS0lX14HvDKL1zMt4USW6pLlyHkhD1AcV2HuemwJeXndunn89ZXLrEBW45PZYEIIIYSY3Kx1jeu6ntM07YPAA4Ad+Jau6zs0TfsMsEXX9V8Bf6Vp2nVADhgGbpmt65mu0WSWFU1BqxLWEvLSMZQYF8IA/vMtZwEwHM/y2+29shwphBBCiCOa1a17uq7fD9w/5rbbSz7/JPDJ2byGozWayFJVUglrqTYrYWqnZKVJ+K9aWo9NgwV1vuN3oUIIIYQ4Kcn8hAoKBZ1YJmeEMPUWmfPCuoxKWKVq14rmIE998goaZTaYEEIIIY5AQlgF8UwOXYeg22GFsIagG7tNoz+aRtMg5K3c99VU5TmelyqEEEKIk9Rcj6g4IcXSatxEwOOwliNDXqc1liLkdeKwy1snhBBCiKMnSaKCmDHzK+hxsHpeFW8/bwGvWlpvhbBaabwXQgghxDGS5cgKzMGrAbcDj9POZ69fC4Df5QDScii3EEIIIY6ZVMIqMJcjg2Om5fvcqhJWIyFMCCGEEMdIQlgFxeXI8uZ7n1OFMlmOFEIIIcSxkhBWQTSVBdRyZCmphAkhhBBipkgIq2DC5UizMb/CoFYhhBBCiOmQEFaB2ZivGvGLfMbXciyREEIIIY6VhLAKYqkcAbcDm00ru91vVcIkhAkhhBDi2EgIqyCWzo5bigTwGpUwCWFCCCGEOFYSwiqIGpWwsaQSJoQQQoiZIiGsglg6R6BCJayl2ovPZac+IAd0CyGEEOLYyMT8CqKpHFUVDui+fv08Ll5ej79ClUwIIYQQYjqkElZBNJUlWCFoOew2GoOeObgiIYQQQpxqJIRVEEvnKjbmCyGEEELMFAlhFUzUmC+EEEIIMVMkhI2RL+gkMvmKjflCCCGEEDNFQtgYxSOL5GgiIYQQQsweCWFjmId3V2rMF0IIIYSYKRLCxjArYbIcKYQQQojZJCFsjFjKXI6UECaEEEKI2SMhbIyoEcJkd6QQQgghZpOEsDGCHgcXLauXo4mEEEIIMauk3DPGxvZavvfuc+f6MoQQQghxipNKmBBCCCHEHJAQJoQQQggxBySECSGEEELMAQlhQgghhBBzQEKYEEIIIcQckBAmhBBCCDEHJIQJIYQQQswBCWFCCCGEEHNAQpgQQgghxByQECaEEEIIMQckhAkhhBBCzAEJYUIIIYQQc0BCmBBCCCHEHNB0XZ/ra5gWTdMGgIOz/G3qgcFZ/h4nG3lPxpP3ZDx5T8rJ+zGevCfjyXsy3qn0nizUdb2h0h0nXQg7HjRN26Lr+sa5vo4Tibwn48l7Mp68J+Xk/RhP3pPx5D0Z73R5T2Q5UgghhBBiDkgIE0IIIYSYAxLCKrtzri/gBCTvyXjynown70k5eT/Gk/dkPHlPxjst3hPpCRNCCCGEmANSCRNCCCGEmAMSwsbQNO0aTdN2aZq2V9O02+b6euaKpmkdmqa9pGnaVk3Tthi31Wqa9ntN0/YYH2vm+jpnk6Zp39I0rV/TtO0lt1V8DzTlq8bfmxc1Tdswd1c+OyZ4P+7QNK3L+HuyVdO015Tc90nj/diladrVc3PVs0vTtDZN0x7WNO1lTdN2aJr2YeP20/LvySTvx2n790TTNI+mac9qmrbNeE8+bdy+SNO0Z4yf/ceaprmM293G13uN+9vn9AeYBZO8J9/RNO1Ayd+T9cbtp+6/N7quyx/jD2AH9gGLARewDThjrq9rjt6LDqB+zG3/BtxmfH4b8K9zfZ2z/B5cDGwAth/pPQBeA/wW0IDzgGfm+vqP0/txB/CxCo89w/j3xw0sMv69ss/1zzAL70kLsMH4PAjsNn720/LvySTvx2n798T4Zx0wPncCzxj/7O8BbjZu/2/g/cbnfwn8//buNNSqKgzj+P9JzWxOUxGbQzAqMq1IjJDmIDBDzEbJoCQjCoQGhAYK/FIRfWg2LUyTyiajjAYkIrRUyqsG0iymUGpFVmpvH/a6tu/p7uunc1b37OcHcvZZe7ld5/Xdh3XX2uuux9PxFODF3J+hhTGZC0zqpn7b3jceCevqDGBDRHwVEX8BC4EJmdv0fzIBmJeO5wGX5mtK80XEMuDnhuKqGEwAnovCJ8Chkoa1pKEtUhGPKhOAhRHxZ0R8DWyguL/aSkRsioiV6fhXYB0wnJrmSQ/xqNL2eZL+r39Lb/ulPwGcA7yUyhtzpDN3XgLOlaTWtLY1eohJlba9b9wJ62o48H3p/Q/0/AXSzgJYKukzSTeksqERsSkd/wgMzdO0rKpiUOfcuTlNEcwpTVHXLh5p2uhUip/qa58nDfGAGueJpD6SVgNbgHcpRvy2RcSuVKX8uffEJJ3fDgxqaYNboDEmEdGZJw+kPHlYUv9U1rZ54k6YVTkrIkYDFwMzJJ1dPhnFGHGtl9Y6BgA8BhwPjAI2AQ9mbU0mkg4EXgZujYhfyufqmCfdxKPWeRIRuyNiFHAExUjfyLwtyq8xJpJOAu6kiM3pwEDg9nwtbA13wrraCBxZen9EKqudiNiYXrcAiym+ODZ3DgGn1y35WphNVQxqmTsRsTl9mf4NPMW/U0m1iYekfhQdjvkR8Uoqrm2edBcP50khIrYBHwBjKabU+qZT5c+9Jybp/CHAT61taeuUYnJRms6OiPgTeJYa5Ik7YV2tAEakVSv7UjwU+XrmNrWcpAMkHdR5DFwArKGIxdRUbSrwWp4WZlUVg9eBa9MqnjOB7aXpqLbV8FzGRIo8gSIeU9JKr2OBEcDyVrev2dKzOs8A6yLiodKpWuZJVTzqnCeSBks6NB0PAM6neFbuA2BSqtaYI525Mwl4P42mto2KmKwv/eAiimfkynnSlvdN371XqY+I2CXpZuAdipWScyKiI3OzchgKLE7PgvYFXoiItyWtABZJuh74FpicsY1NJ2kBMB44XNIPwN3AbLqPwVsUK3g2AL8D17W8wU1WEY/xaRl5UKyovREgIjokLQLWAruAGRGxO0Ozm20ccA3wRXq+BeAu6psnVfG4osZ5MgyYJ6kPxcDHooh4U9JaYKGk+4FVFJ1X0uvzkjZQLISZkqPRTVYVk/clDaZYBbkamJ7qt+1949+Yb2ZmZpaBpyPNzMzMMnAnzMzMzCwDd8LMzMzMMnAnzMzMzCwDd8LMzMzMMnAnzMxqS9I3kg7fS527WtUeM6sXd8LMzHrmTpiZNYU7YWbWa0k6RtKa0vuZku6R9KGkRyStlrRG0hnp/CBJSyV1SHqa4pdCdv7dV9OG9R2dm9ZLmg0MSNeZn8qulrQ8lT2RNiLuI2lu+re+kHRbayNhZr2RO2Fm1q72TxsE3wTMSWV3Ax9FxIkUe6IeVao/LSLGAKcBt0gaFBF3ADsiYlREXCXpBOByYFy69m7gKoqNqYdHxEkRcTLFvndmZj3ytkVm1q4WAETEMkkHp73qzgYuS+VLJG0t1b9F0sR0fCTFPoaNGyefC4wBVqRtvQZQbM79BnCcpEeBJcDSpnwiM2sr7oSZWW+2i64j+vuVjhv3ZKvco03SeOA8YGxE/C7pw4Zr7akKzIuIO7u5xinAhRT73U0Gpu29+WZWZ56ONLPebDMwJD3r1R+4pHTucgBJZwHbI2I7sAy4MpVfDByW6h4CbE0dsJHAmaXr7JTULx2/B0ySNCRdY6Cko9MKy30i4mVgFjC6GR/WzNqLR8LMrNeKiJ2S7gOWAxuB9aXTf0haBfTj31Gpe4EFkjqAj4HvUvnbwHRJ64AvgU9K13kS+FzSyvRc2CxgqaR9gJ3ADGAH8GwqA/jPSJmZWSNFVI7Qm5n1Smk6cWZEfJq7LWZmVTwdaWZmZpaBR8LMzMzMMvBImJmZmVkG7oSZmZmZZeBOmJmZmVkG7oSZmZmZZeBOmJmZmVkG7oSZmZmZZfAPJ3oK10D9WUAAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -2120,7 +1733,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "id": "cac26e8e", "metadata": {}, "outputs": [ @@ -2128,8 +1741,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "evaluating...: 100%|██████████| 49/49 [00:03<00:00, 15.38it/s]\n", - "test_loss: 0.353, test_acc: 0.857\n" + "evaluating...: 100%|██████████| 49/49 [00:03<00:00, 15.72it/s]\n", + "test_loss: 0.359, test_acc: 0.854\n" ] } ], @@ -2146,7 +1759,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "id": "b22e040a", "metadata": {}, "outputs": [], @@ -2164,17 +1777,17 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "id": "9cfa14eb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0, 0.9999740123748779)" + "(0, 0.9999850988388062)" ] }, - "execution_count": 54, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -2187,17 +1800,17 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "id": "1da60d90", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(1, 0.9999997615814209)" + "(1, 0.9999994039535522)" ] }, - "execution_count": 55, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -2210,17 +1823,17 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "id": "4bee6190", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(1, 0.765370786190033)" + "(1, 0.6572516560554504)" ] }, - "execution_count": 56, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -2233,17 +1846,17 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "id": "e3d55c92", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(1, 0.765370786190033)" + "(1, 0.6572516560554504)" ] }, - "execution_count": 57, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } diff --git a/assets/nbow_model.png 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