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import keyring as kr |
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import os |
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import random |
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import json |
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import re |
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import sys |
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import time |
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from collections import defaultdict |
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from functools import reduce |
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import codefast as cf |
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import joblib |
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import numpy as np |
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import pandas as pd |
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from rich import print |
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from typing import List, Union, Callable, Set, Dict, Tuple, Optional, Any |
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from pydantic import BaseModel |
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import asyncio |
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import aiohttp |
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import aioredis |
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from codefast.patterns.pipeline import Pipeline, BeeMaxin |
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from datasets import load_dataset |
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class DataLoader(BeeMaxin): |
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def __init__(self) -> None: |
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super().__init__() |
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def process(self): |
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files = [] |
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for f in cf.io.walk('jsons/'): |
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files.append(f) |
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return files |
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class ToCsv(BeeMaxin): |
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def to_csv(self, json_file: str): |
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texts, labels = [], [] |
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with open(json_file, 'r') as f: |
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for line in f: |
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line = json.loads(line) |
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texts.append(line['text']) |
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_label = ' '.join(line['labels']) |
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labels.append(_label) |
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task_name = cf.io.basename(json_file).replace('.json', '') |
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return pd.DataFrame({'text': texts, 'labels': labels, |
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'task_name': task_name}) |
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def process(self, files: List[str]): |
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""" Merge all ner data into a train.csv |
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""" |
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df = pd.DataFrame() |
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for f in files: |
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cf.info({ |
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'message': f'processing {f}' |
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}) |
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newdf = self.to_csv(f) |
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df = pd.concat([df, newdf], axis=0) |
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df.to_csv('train.csv', index=False) |
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df.sample(10).to_csv('dev.csv', index=False) |
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if __name__ == '__main__': |
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pl = Pipeline( |
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[ |
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('dloader', DataLoader()), |
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('csv converter', ToCsv()) |
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] |
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) |
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pl.gather() |
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