177 lines
3.5 KiB
Plaintext
177 lines
3.5 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Data chunking for effectiveness\n",
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"\n",
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"In our data, facebook user called Robert Fico has a lot of samples.\n",
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"For efficiency, this notebook chunks those data in 4 parts."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### JSONL file loading and creation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_jsonl(file_path):\n",
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" with open(file_path, 'r', encoding='utf-8') as file:\n",
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" return [json.loads(line) for line in file]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def create_jsonl(filename, new_dataset):\n",
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" with open(f'{filename}l', 'w') as jsonl_file:\n",
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" for item in new_dataset:\n",
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" jsonl_file.write(json.dumps(item) + '\\n')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"fico = load_jsonl('jsonl_data/robert_fico_data.jsonl')"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Split data into 4 parts equal parts"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"135155"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"num_samples = len(fico)\n",
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"chunk_size = int(num_samples / 4)\n",
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"\n",
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"num_samples"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"False"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chunk_size * 4 == num_samples # we have lost one sample, because our dataset has odd number of samples"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Actual chunking algorithm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"chunk_arr = []\n",
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"for chunks in range(0, 4):\n",
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" chunk_arr.append(\n",
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" fico[chunk_size * chunks: chunk_size * (chunks + 1)]\n",
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" )"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Write chunked data to disk in a for loop"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"for index, data in enumerate(chunk_arr):\n",
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" create_jsonl(f'jsonl_data/fico_chunk_{index}.json', data)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "sentiment",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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