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 full_predict_tr_results.json from Yannis98/tmp_natqa: direct link, hf CLI and curl.
- Browser
- Download file 19.5 MB
-
https://huggingface.co/Yannis98/tmp_natqa/resolve/main/full_predict_tr_results.json
- Command line
-
hf download hf://Yannis98/tmp_natqa/full_predict_tr_results.json
-
curl -L -o full_predict_tr_results.json https://huggingface.co/Yannis98/tmp_natqa/resolve/main/full_predict_tr_results.json
19.5 MB
- Xet hash:
- 06b92c5ed8c6655f6d0ec036d03620cf686567ce116735a782f873fa0b433068
- Size of remote file:
- 19.5 MB
- SHA256:
- f0619634de38a5739f09f9f7d429a2d857d2c8e9acd07a86bb848f7dc826b2f3
路
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