Instructions to use bclavie/edubert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bclavie/edubert with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bclavie/edubert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
language:
- en
tags:
- education
- learning analytics
- educational data mining
Model Card for Model ID
This is the EduBERT model used in the EduBERT: Pretrained Deep Language Models for Learning Analytics from LAK20. It is a fine-tuned version of BERT-base on educational data.
Model Description
We originally trained this model to support Learning Analytics task, showing it performed well on well-known educational text classification task.
Bias, Risks, and Limitations
The model is provided as-is, and trained on the data described in the paper. Learning Analytics is a complex field, and decisions should not be taken fully automatically by models. This model should be used for analysis and to inform only.
Citation
BibTeX:
@inproceedings{clavié2019edubert,
title={EduBERT: Pretrained Deep Language Models for Learning Analytics},
author={Benjamin Clavié and Kobi Gal},
year={2020},
booktitle={Companion Proceedings of the The 10th international Learning Analytics & Knowledge (LAK20)}
}