Instructions to use CambridgeMolecularEngineering/bert-base-cased-scsmall-scqa2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CambridgeMolecularEngineering/bert-base-cased-scsmall-scqa2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="CambridgeMolecularEngineering/bert-base-cased-scsmall-scqa2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("CambridgeMolecularEngineering/bert-base-cased-scsmall-scqa2") model = AutoModelForQuestionAnswering.from_pretrained("CambridgeMolecularEngineering/bert-base-cased-scsmall-scqa2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4f749c7c481d6f25b440e91a8230e2b8d3514aa40ac75a55a191e1947f2d941
- Size of remote file:
- 431 MB
- SHA256:
- 53223b2d6eee2e752081a88a9f3836799ff334a1dca32d1cc7bf13a71c220f74
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.