Skillner

🚀 Results

This model is a fine-tuned version of jjzha/jobbert-base-cased for the task of token classification. It solves the problem of accurately classifying tokens in job descriptions, providing valuable information for job analysis.

🚀 Quick Start

This model is ready to use for token - classification tasks. You can load it from Hugging Face and start making predictions.

✨ Features

• Based on a BERT transformer model pretrained on job - advert sentences.
• Capable of predicting labels for tokens in job descriptions, including 'B - SKILL', 'I - SKILL' or 'O' (not a skill).
• Achieved high accuracy (0.9701) on the evaluation set.

📦 Installation

The installation steps are not provided in the original document, so this section is skipped.

💻 Usage Examples

Usage examples are not provided in the original document, so this section is skipped.

📚 Documentation

Model description

The base model (jjzha/jobbert-base-cased) is a BERT transformer model, pretrained on a corpus of ~3.2 million sentences from job adverts for the objective of Masked Language Modelling (MLM). A token classification head is added to the top of the model to predict a label for every token in a given sequence. In this instance, it is predicting a label for every token in a job description, where the label is either a 'B - SKILL', 'I - SKILL' or 'O' (not a skill).

Training and evaluation data

The model was trained on 4112 job advert sentences.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.34.1
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1

🔧 Technical Details

The model uses a BERT - based architecture with a token classification head. The base model is pretrained on a large corpus of job - advert sentences using Masked Language Modelling (MLM). During fine - tuning, it is trained on a specific dataset of job advert sentences with the goal of predicting token labels in job descriptions. The hyperparameters are carefully selected to achieve good performance on the evaluation set.

📄 License

License information is not provided in the original document, so this section is skipped.