Question Answering
Transformers
PyTorch
TensorFlow
Safetensors
English
deberta-v2
deberta
deberta-v3
squad
squad_v2
Eval Results (legacy)
Instructions to use sjrhuschlee/deberta-v3-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjrhuschlee/deberta-v3-base-squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="sjrhuschlee/deberta-v3-base-squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sjrhuschlee/deberta-v3-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("sjrhuschlee/deberta-v3-base-squad2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "bos_token": "[CLS]", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "pad_token": "[PAD]", | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "sp_model_kwargs": {}, | |
| "split_by_punct": false, | |
| "tokenizer_class": "DebertaV2Tokenizer", | |
| "truncation_side": "right", | |
| "unk_token": "[UNK]", | |
| "vocab_type": "spm" | |
| } | |