Question Answering
Transformers
PyTorch
Safetensors
English
roberta
optoelectronics
science
data-mining
Instructions to use Dingyun-Huang/oe-roberta-base-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dingyun-Huang/oe-roberta-base-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Dingyun-Huang/oe-roberta-base-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Dingyun-Huang/oe-roberta-base-qa") model = AutoModelForQuestionAnswering.from_pretrained("Dingyun-Huang/oe-roberta-base-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3140c73d5c254d85311d252a27675ac43c04f97d504d376d19e2c7628b2c4b09
- Size of remote file:
- 496 MB
- SHA256:
- 9aa5811570995080a3151277c578170e065e2432660b78e56c24810fecf75936
路
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