Instructions to use hf-internal-testing/tiny-random-SplinterForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SplinterForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-SplinterForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SplinterForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-SplinterForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92706ae0630c23d80e9a4617073ab3dd175f3455b1d85a79b19bd9797207436b
- Size of remote file:
- 3.96 MB
- SHA256:
- 70611a10fe62f534a68b1b7d7dad6ac6b822cfa2440620bcf4f2f8e22254b4c2
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.