File size: 1,293 Bytes
333c786 1fdbb28 333c786 79d6f9f 771bdc6 79d6f9f 4690d80 79d6f9f 333c786 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ---
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) |