Feature Extraction
Transformers
Safetensors
Vietnamese
deberta-v2
vietnamese
summarization-evaluation
efficient-evaluation
multi-criteria-evaluation
evaluation
reward-model
rlhf
cross-encoder
Instructions to use phuongntc/Multi_EvalSumViet2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phuongntc/Multi_EvalSumViet2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="phuongntc/Multi_EvalSumViet2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("phuongntc/Multi_EvalSumViet2") model = AutoModel.from_pretrained("phuongntc/Multi_EvalSumViet2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "regression": "MSE (3 heads)", | |
| "ranking": "pairwise hinge in-document", | |
| "rank_margin": 0.05, | |
| "rank_weight": 0.35, | |
| "task_weights_note": "equal per-criterion in rank aggregation" | |
| } |