Instructions to use dflcmu/fine_tune_spatial_validation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dflcmu/fine_tune_spatial_validation 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="dflcmu/fine_tune_spatial_validation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dflcmu/fine_tune_spatial_validation") model = AutoModelForQuestionAnswering.from_pretrained("dflcmu/fine_tune_spatial_validation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dflcmu/fine_tune_spatial_validation: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/dflcmu/fine_tune_spatial_validation/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://dflcmu/fine_tune_spatial_validation@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dflcmu/fine_tune_spatial_validation/resolve/refs%2Fpr%2F1/training_args.bin
3.64 kB
- Xet hash:
- 61ce47daa97514a6fb29112047817582cf7b34667a3af8ebd86f0ad05454939d
- Size of remote file:
- 3.64 kB
- SHA256:
- 227f23d4dd9d72d4815632bc6eaa3e1ceda61a570580779763c32f2a3fbb1bf1
路
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