Instructions to use Yannis98/tmp_natqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yannis98/tmp_natqa 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="Yannis98/tmp_natqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Yannis98/tmp_natqa") model = AutoModelForQuestionAnswering.from_pretrained("Yannis98/tmp_natqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from Yannis98/tmp_natqa: direct link, hf CLI and curl.
- Browser
- Download file 21.9 MB
-
https://huggingface.co/Yannis98/tmp_natqa/resolve/main/all_results.json
- Command line
-
hf download hf://Yannis98/tmp_natqa/all_results.json
-
curl -L -o all_results.json https://huggingface.co/Yannis98/tmp_natqa/resolve/main/all_results.json
21.9 MB
- Xet hash:
- f36cadadc4a383100a46d4bc8c765c8d978bc935b65bde73252a4cd6cf8c3804
- Size of remote file:
- 21.9 MB
- SHA256:
- 3daba1339ccc9a5503bcd2e6a6e862436eacc5d24317d614d14e879d36e299af
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.