Text Classification
Transformers
PyTorch
Vietnamese
vietnamese
summarization
evaluation
cross-encoder
research
Instructions to use phuongntc/MultiEvalVietSum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phuongntc/MultiEvalVietSum with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="phuongntc/MultiEvalVietSum")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("phuongntc/MultiEvalVietSum", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "multievalvietsum", | |
| "repo_id": "phuongntc/MultiEvalVietSum", | |
| "backbone_name": "jhu-clsp/mmBERT-base", | |
| "max_len": 2048, | |
| "summary_max_len": 192, | |
| "pooling": "cls_plus_mean", | |
| "outputs": [ | |
| "faithfulness", | |
| "coherence", | |
| "relevance" | |
| ], | |
| "notes": [ | |
| "Custom evaluator architecture on top of a Hugging Face backbone", | |
| "Use modeling_multievalvietsum.py to load the model correctly" | |
| ] | |
| } |