metadata
license: apache-2.0
language: nl
tags:
- BERTje
- Filtering
- Data Cleaning
Model description
This model was created with the intention of easily being able to filter large synthetic datasets in the Dutch language. It was mostly trained to pick out strings with a lot of repitition, weird grammar or refusals specifically, returning either ["Correct","Error","Refusal"]
THIS IS NOT THE FINAL VERSION, MORE ITERATIONS IN THE NEXT FEW WEEKS
How to use
from transformers import AutoTokenizer, BertForSequenceClassification, pipeline
import json
model = BertForSequenceClassification.from_pretrained("Kalamazooter/DutchDatasetCleaner_Bertje")
tokenizer = AutoTokenizer.from_pretrained("Kalamazooter/DutchDatasetCleaner_Bertje", model_max_len=512)
text_classification = pipeline(
"text-classification",
model=model,
tokenizer=tokenizer,
)
tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
ErrorThreshold = 0.8 #model is slightly trigger happy on the error class, modify this value to your needs
Dataset = "Base_Dataset"
with open(Dataset+".jsonl","r") as DirtyDataset:
lines = DirtyDataset.readlines()
for line in lines:
DatasetDict = json.loads(line)
output = text_classification(DatasetDict['text'],**tokenizer_kwargs)
label = output[0]['label']
score = output[0]['score']
if label == 'Refusal':
with open(Dataset+"_Refused.jsonl","a") as RefusalDataset:
RefusalDataset.writelines([line])
if label == 'Error' and score > ErrorThreshold:
with open(Dataset+"_Error.jsonl","a") as ErrorDataset:
ErrorDataset.writelines([line])
if label == 'Correct' or (label == 'Error' and score < ErrorThreshold):
with open(Dataset+"_Clean.jsonl","a") as CorrectDataset:
CorrectDataset.writelines([line])