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 eval_nbest_predictions.json from Yannis98/tmp_natqa: direct link, hf CLI and curl.
- Browser
- Download file 78.8 MB
-
https://huggingface.co/Yannis98/tmp_natqa/resolve/main/eval_nbest_predictions.json
- Command line
-
hf download hf://Yannis98/tmp_natqa/eval_nbest_predictions.json
-
curl -L -o eval_nbest_predictions.json https://huggingface.co/Yannis98/tmp_natqa/resolve/main/eval_nbest_predictions.json
78.8 MB
- Xet hash:
- d90def2bd5c069e9cfb67351e017e4d3d8900b9560e0c5d722efb28e6f9875b0
- Size of remote file:
- 78.8 MB
- SHA256:
- 13b3a46e5429beee81d5a1e3d7dab5623aeb67c2c30b0d2bb218e9a541229ecb
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.