Instructions to use srcocotero/mini-bert-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use srcocotero/mini-bert-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="srcocotero/mini-bert-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("srcocotero/mini-bert-qa") model = AutoModelForQuestionAnswering.from_pretrained("srcocotero/mini-bert-qa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7292743223685ac391eb18825c1c3aa46f1e17fb04c67ac6bf7f87444ee4eeb
- Size of remote file:
- 44.4 MB
- SHA256:
- aeaa7cdd6bbe95fc25fb2d4e5d7d73fffef76df91450f6fa6ee02066f6b0395d
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.