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--- |
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library_name: transformers |
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license: mit |
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datasets: |
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- ahmedheakl/resume-atlas |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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pipeline_tag: text-classification |
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--- |
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# How to use |
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In this example, we do an inference on a sample from our dataset (_ResumeAtlas_). You can increase `max_length` for more accurate predictions. |
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```python |
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!pip install datasets |
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import numpy as np |
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import torch |
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from transformers import BertForSequenceClassification, BertTokenizer |
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from datasets import load_dataset |
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from sklearn import preprocessing |
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dataset_id='ahmedheakl/resume-atlas' |
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model_id='ahmedheakl/bert-resume-classification' |
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label_column = "Category" |
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num_labels=43 |
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output_attentions=False |
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output_hidden_states=False |
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do_lower_case=True |
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add_special_tokens=True |
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max_length=512 |
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pad_to_max_length=True |
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return_attention_mask=True |
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truncation=True |
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ds = load_dataset(dataset_id, trust_remote_code=True) |
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le = preprocessing.LabelEncoder() |
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le.fit(ds['train'][label_column]) |
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tokenizer = BertTokenizer.from_pretrained(model_id, do_lower_case=do_lower_case) |
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model = BertForSequenceClassification.from_pretrained( |
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model_id, |
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num_labels = num_labels, |
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output_attentions = output_attentions, |
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output_hidden_states = output_hidden_states, |
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) |
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model = model.to('cuda').eval() |
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sent = ds['train'][0]['Text'] |
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encoded_dict = tokenizer.encode_plus( |
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sent, |
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add_special_tokens=add_special_tokens, |
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max_length=max_length, |
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pad_to_max_length=pad_to_max_length, |
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return_attention_mask=return_attention_mask, |
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return_tensors='pt', |
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truncation=truncation, |
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) |
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input_ids = encoded_dict['input_ids'].to('cuda') |
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attention_mask = encoded_dict['attention_mask'].to('cuda') |
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outputs = model( |
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input_ids, |
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token_type_ids=None, |
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attention_mask=attention_mask |
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) |
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label_id = np.argmax(outputs['logits'].cpu().detach().tolist(), axis=1) |
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print(f'Predicted: {le.inverse_transform(label_id)[0]} | Ground: {ds["train"][0][label_column]}') |
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``` |
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# Model Card for Model ID |
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**Please see paper & code for more information:** |
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- https://github.com/noran-mohamed/Resume-Classification-Dataset |
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- https://arxiv.org/abs/2406.18125 |
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## Citation |
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**BibTeX:** |
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``` |
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@article{heakl2024resumeatlas, |
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title={ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models}, |
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author={Heakl, Ahmed and Mohamed, Youssef and Mohamed, Noran and Sharkaway, Ali and Zaky, Ahmed}, |
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journal={arXiv preprint arXiv:2406.18125}, |
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year={2024} |
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} |
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``` |
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**APA:** |
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``` |
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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 |
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``` |
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## Model Card Authors [optional] |
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Email: [email protected] |
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Linkedin: https://linkedin.com/in/ahmed-heakl |
