Instructions to use hf-internal-testing/tiny-random-FunnelForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FunnelForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-FunnelForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FunnelForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-FunnelForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98a10b717110c3347aa9af5457b599eea0003537fb3daa899ba665d5da5f63e6
- Size of remote file:
- 315 kB
- SHA256:
- 442469379b990318673506a2b2d36b41a073441d19fd0076b5aa52430b6a0aa8
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.