metadata
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': all-domains
'1': it-domain
- name: images
list: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 115281378.25917527
num_examples: 1585
download_size: 114806537
dataset_size: 115281378.25917527
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Extracted lists of pages from PDF resumes and the PDF texts.
Created using this code:
import io
import PIL.Image
from datasets import load_dataset
def render(pdf):
images = []
for page in pdf.pages:
buffer = io.BytesIO()
page.to_image(height=840).save(buffer)
images.append(PIL.Image.open(buffer))
return images
def extract_text(pdf):
return "\n".join(page.extract_text() for page in pdf.pages)
ds = load_dataset("d4rk3r/resumes-raw-pdf", split="train")
ds = ds.map(lambda x: {
"images": render(x["pdf"]),
"text": extract_text(x["pdf"])
}, remove_columns=["pdf"])
ds = ds.filter(lambda x: len(x["text"].strip()) > 0)
ds.push_to_hub("lhoestq/resumes-raw-pdf-for-ocr")