Text Classification
Transformers
PyTorch
distilbert
fine-tuning
resume classification
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("oussama120/Resume_Sentence_Classification")
model = AutoModelForSequenceClassification.from_pretrained("oussama120/Resume_Sentence_Classification")Quick Links
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:
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)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oussama120/Resume_Sentence_Classification")