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")# pip install -U transformers accelerate # 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
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Initial release: backbone (videberta), trunk+3 heads, configs, loader, README
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