Instructions to use radlab/polish-qa-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radlab/polish-qa-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="radlab/polish-qa-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("radlab/polish-qa-v2") model = AutoModelForQuestionAnswering.from_pretrained("radlab/polish-qa-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bcfa558db5fe655c8f8d0e1b17a4d77deb1c9851099dd35d77e9a6c5ccf01d57
- Size of remote file:
- 1.74 GB
- SHA256:
- 69af409da11304e387b7e671c8bd5aa05e165e8197b638087a866f78ebeda807
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.