---
license: apache-2.0
tags:
- text-classification
- fine-tuning
- resume classification
library_name: transformers
---
# DistilBERT Resume Classification Model

This repository contains a fine-tuned DistilBERT model for classifying resume sentences into predefined categories. The model is trained on a dataset of resumes and can classify sentences into categories such as Personal Information, Experience, Summary, Education, Qualifications & Certificates, Skills, and Objectives.

## Model Details

- **Model:** DistilBERT (base-uncased)
- **Fine-tuned on:** Custom resume dataset (ganchengguang/resume_seven_class)
- **Number of classes:** 7

## Categories

The model can classify sentences into the following categories:
- Personal Information
- Experience
- Summary
- Education
- Qualifications & Certificates
- Skills
- Objectives

## Usage

### Load the Model and Tokenizer

To use the model and tokenizer, you can load them from the Hugging Face Hub as follows:

```python
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification

# Load the model and tokenizer
model_name = "oussama120/Resume_Sentence_Classification"
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)