Instructions to use hf-tiny-model-private/tiny-random-SplinterForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-SplinterForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-SplinterForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SplinterForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-SplinterForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06902fa6c9896969ec5545c429e78127778e44ae30056674e7663d8cd4cab3ab
- Size of remote file:
- 3.96 MB
- SHA256:
- f9b58f787d8f162d4c1c95e30584b67d491e9b39d77b188595601a5d087be638
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