How to use
In this example, we do an inference on a sample from our dataset (ResumeAtlas). You can increase max_length for more accurate predictions.
!pip install datasets
import numpy as np
import torch
from transformers import BertForSequenceClassification, BertTokenizer
from datasets import load_dataset
from sklearn import preprocessing
dataset_id='ahmedheakl/resume-atlas'
model_id='ahmedheakl/bert-resume-classification'
label_column = "Category"
num_labels=43
output_attentions=False
output_hidden_states=False
do_lower_case=True
add_special_tokens=True
max_length=512
pad_to_max_length=True
return_attention_mask=True
truncation=True
ds = load_dataset(dataset_id, trust_remote_code=True)
le = preprocessing.LabelEncoder()
le.fit(ds['train'][label_column])
tokenizer = BertTokenizer.from_pretrained(model_id, do_lower_case=do_lower_case)
model = BertForSequenceClassification.from_pretrained(
model_id,
num_labels = num_labels,
output_attentions = output_attentions,
output_hidden_states = output_hidden_states,
)
model = model.to('cuda').eval()
sent = ds['train'][0]['Text']
encoded_dict = tokenizer.encode_plus(
sent,
add_special_tokens=add_special_tokens,
max_length=max_length,
pad_to_max_length=pad_to_max_length,
return_attention_mask=return_attention_mask,
return_tensors='pt',
truncation=truncation,
)
input_ids = encoded_dict['input_ids'].to('cuda')
attention_mask = encoded_dict['attention_mask'].to('cuda')
outputs = model(
input_ids,
token_type_ids=None,
attention_mask=attention_mask
)
label_id = np.argmax(outputs['logits'].cpu().detach().tolist(), axis=1)
print(f'Predicted: {le.inverse_transform(label_id)[0]} | Ground: {ds["train"][0][label_column]}')
Model Card for Model ID
Please see paper & code for more information:
Citation
BibTeX:
@article{heakl2024resumeatlas,
title={ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models},
author={Heakl, Ahmed and Mohamed, Youssef and Mohamed, Noran and Sharkaway, Ali and Zaky, Ahmed},
journal={arXiv preprint arXiv:2406.18125},
year={2024}
}
APA:
Heakl, A., Mohamed, Y., Mohamed, N., Sharkaway, A., & Zaky, A. (2024). ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2406.18125
Model Card Authors [optional]
Email: ahmed.heakl@ejust.edu.eg Linkedin: https://linkedin.com/in/ahmed-heakl