Extended_distilBERT Open Source Model - Free Deployment to Boost the Accurate Classification Task of Resume Paragraphs

Extended Distilbert Finetuned Resumes Sections

Developed by has-abi

This model is a fine-tuned version of Geotrend/distilbert-base-en-fr-cased on an unspecified dataset, primarily used for resume section classification tasks.

Text Classification

Transformers

Open Source License:Apache-2.0 #Resume Parsing #High-precision Classification #Bilingual Processing

Downloads 187

Release Time : 9/9/2022

Model Overview

This is a fine-tuned DistilBERT model specifically designed for classifying resume text. The model performs excellently on the evaluation set, achieving an F1 score of 0.9735.

Model Features

Efficient Fine-tuning

Based on the DistilBERT architecture, it reduces model size and computational requirements while maintaining performance.

Multilingual Support

Supports processing text in both English and French.

High Accuracy

Achieved an accuracy of 0.9715 and an F1 score of 0.9735 on the evaluation set.

Model Capabilities

Resume Text Classification

Multilingual Text Processing

Efficient Inference

Use Cases

Human Resources

Automatic Resume Classification

Automatically identifies and classifies different sections in resumes (e.g., education background, work experience).

Classification accuracy reached 97.15%.

Document Processing

Structured Document Parsing

Converts unstructured resume text into structured data.

🚀 extended_distilBERT-finetuned-resumes-sections

This model is a fine - tuned version of Geotrend/distilbert-base-en-fr-cased, which can achieve excellent performance on specific tasks.

🚀 Quick Start

This model is a fine-tuned version of Geotrend/distilbert-base-en-fr-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0321
F1: 0.9735
Roc Auc: 0.9850
Accuracy: 0.9715

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.0283	1.0	2213	0.0247	0.9610	0.9763	0.9539
0.0153	2.0	4426	0.0223	0.9634	0.9789	0.9593
0.01	3.0	6639	0.0199	0.9702	0.9835	0.9675
0.0073	4.0	8852	0.0218	0.9710	0.9838	0.9690
0.0063	5.0	11065	0.0244	0.9706	0.9835	0.9684
0.0037	6.0	13278	0.0251	0.9700	0.9833	0.9684
0.004	7.0	15491	0.0273	0.9712	0.9837	0.9693
0.003	8.0	17704	0.0266	0.9719	0.9841	0.9695
0.0027	9.0	19917	0.0294	0.9697	0.9831	0.9679
0.0014	10.0	22130	0.0275	0.9714	0.9844	0.9690
0.0016	11.0	24343	0.0299	0.9714	0.9839	0.9697
0.0013	12.0	26556	0.0297	0.9719	0.9852	0.9697
0.0006	13.0	28769	0.0312	0.9711	0.9843	0.9697
0.0004	14.0	30982	0.0305	0.9731	0.9849	0.9720
0.0004	15.0	33195	0.0312	0.9723	0.9845	0.9704
0.0005	16.0	35408	0.0331	0.9716	0.9843	0.9697
0.0006	17.0	37621	0.0321	0.9735	0.9850	0.9715
0.0004	18.0	39834	0.0322	0.9731	0.9850	0.9711
0.0003	19.0	42047	0.0332	0.9722	0.9847	0.9706
0.0004	20.0	44260	0.0334	0.9720	0.9846	0.9704

Framework versions

Transformers 4.21.3
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1

📄 License

This project is licensed under the Apache-2.0 license.

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