semantic-cleaning 0.0.3

Tools for semantic cleaning of a test dataset

semantic-cleaning

This file will become your README and also the index of your documentation.

Install

pip install semantic_cleaning

How to use

import os
from tqdm.auto import tqdm
from typing import List, Dict, Set, Union, Callable
import torch
from torch.utils.data import DataLoader
from datasets import Dataset, load_dataset
import numpy as np
from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
import torch.nn.functional as F
import transformers

Processing a dataset to get a sentence for QA or comment and response etc.

data = load_dataset("0-hero/OIG-small-chip2")
_ = preprocess_data(data,schema = ":{user} :{chip2}")
data['train']['_merged'][0]

2

Compute the embadding fot the sentences

tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2').to('cuda')
embedding = compute_embeddings(data = data, embedding_model = model, tokenizer = tokenizer, batch_size = 64, num_workers =16, dataset_feature  = '_merged'):

We can get the indicis of all the duplicated lines with the folowing command:

to_delete = deduplicate_embeddings(embedded =embeddeing, epsilon=1e-2, batch_size=20000)

The full process could be run like this

  deduplicated = deduplicate_dataset(
      dataset = data['train'], 
      model = model, 
      tokenizer = tokenizer,
      epsilon = 1e-2, 
      model_batch_size = 64, 
      deduplication_batch_size = 20000, 
      num_workers = 16,
      dataset_feature = '_merged'
  )
  print (f"cleaned:{(1-len(deduplicated)/len(data['train']))*100:.2f}:%")

And deduplicated can be pushed back to the hub or saved on local drive