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 # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="radlab/polish-qa-v2")# pip install -U transformers accelerate # 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
Download training_args.bin from radlab/polish-qa-v2: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/radlab/polish-qa-v2/resolve/main/training_args.bin
- Command line
-
hf download hf://radlab/polish-qa-v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/radlab/polish-qa-v2/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 72f6171e67c8c857e9ab2cf4b0b6ee30bed691c2a59af48d7bb412d8040157a5
- Size of remote file:
- 4.03 kB
- SHA256:
- 973bcaae82348f96bdcaa6e231e30e8448cebd8d5f665f6fd28a98c2cc894733
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.