Instructions to use dflcmu/fine_tune_spatial_validation_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dflcmu/fine_tune_spatial_validation_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="dflcmu/fine_tune_spatial_validation_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dflcmu/fine_tune_spatial_validation_v2") model = AutoModelForQuestionAnswering.from_pretrained("dflcmu/fine_tune_spatial_validation_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dflcmu/fine_tune_spatial_validation_v2: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/dflcmu/fine_tune_spatial_validation_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://dflcmu/fine_tune_spatial_validation_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dflcmu/fine_tune_spatial_validation_v2/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 3941d1da961cf9f4c70846e1825fb35a7b9fad4f3a869f9cebc567e7569fd0e7
- Size of remote file:
- 3.64 kB
- SHA256:
- 34f0f6fb830f489caec2365d35f581678fa9730f9ef47173d4bf75a0b5893dcf
路
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