Text Classification
Transformers
Safetensors
Korean
roberta
DPR
Classification
RAG
text-embeddings-inference
Instructions to use NHNDQ/SelectionModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NHNDQ/SelectionModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NHNDQ/SelectionModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NHNDQ/SelectionModel") model = AutoModelForSequenceClassification.from_pretrained("NHNDQ/SelectionModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-4.0 | |
| language: | |
| - ko | |
| tags: | |
| - DPR | |
| - Classification | |
| - RAG | |
| ## Model Details | |
| * Model Description: Selection Model | |
| * Developed by: Jisu Kim, TakSung Heo, Minsu Jeong, and Juhwan Lee | |
| * Model Type: Classification | |
| * License: CC-BY-4.0 | |
| ## Dataset | |
| * [AI-hub dataset](https://www.aihub.or.kr/) | |
| ## Uses | |
| This model can be used for context extraction. | |
| ## Source Code | |
| [SelectionModel](https://github.com/trailerAI/SelectionModel/blob/main/README.md) |