Question Answering
Transformers
PyTorch
Safetensors
English
deberta-v2
deberta
deberta-v3
mdeberta
squad
squad_v2
Eval Results (legacy)
Instructions to use sjrhuschlee/mdeberta-v3-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjrhuschlee/mdeberta-v3-base-squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="sjrhuschlee/mdeberta-v3-base-squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sjrhuschlee/mdeberta-v3-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("sjrhuschlee/mdeberta-v3-base-squad2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee8bfadf27e435765ded35a338cd54ee348902fdacaecf71e03dfa4e6b753092
- Size of remote file:
- 1.11 GB
- SHA256:
- 5a64787b4848ff3d069afc81321c9048b6bab568f13d84b8f2037d7491eb61df
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.