ERNIE is an open-source toolkit for augmenting pre-trained language models with knowledge graph representations, targeting researchers and practitioners in Natural Language Processing. It enhances model performance on knowledge-intensive tasks by integrating entity information from knowledge graphs.

How It Works

ERNIE enhances pre-trained language models by incorporating knowledge graph embeddings. This approach aims to imbue models with a richer understanding of entities and their relationships, leading to improved performance on tasks like entity typing and relation classification. The toolkit provides pre-trained ERNIE models and detailed instructions for fine-tuning on specific downstream tasks.

Quick Start & Requirements

• Install: pip install tagme (for entity linking in new tasks).
• Prerequisites: PyTorch >= 0.4.1, Python 3, tqdm, boto3, requests, apex (for fp16).
• Setup: Requires downloading large datasets (Wikidump, anchor2id, pre-trained models, annotated datasets) and potentially pre-training the model, which can take significant time and resources (e.g., 8 NVIDIA-2080Ti for ~1 day).
• Links: ACL 2019 Paper, Example Usage

Highlighted Details

• Achieves improved accuracy and F1 scores over BERT on entity typing and relation classification tasks.
• Supports fine-tuning for tasks like FewRel, TACRED, FIGER, and OpenEntity.
• Includes utilities for data preprocessing, pre-training, and evaluation.
• Offers a quick-start example for using ERNIE with masked language modeling.

Maintenance & Community

• ERNIE is a sub-project of OpenSKL.
• The project is associated with THU (Tsinghua University).
• Citation details are provided for the ACL 2019 paper.

Licensing & Compatibility

• The ERNIE toolkit itself is not explicitly licensed in the README.
• OpenKE resources (knowledge graph embeddings) are provided under the MIT license.
• Compatibility with commercial or closed-source projects is not specified.

Limitations & Caveats

The setup process for pre-training is resource-intensive and time-consuming. The README does not explicitly state the license for the ERNIE toolkit code itself, which may impact commercial use. Entity linking for new tasks relies on external tools like TAGME.