Instructions to use hf-tiny-model-private/tiny-random-RoCBertForQuestionAnswering 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-RoCBertForQuestionAnswering 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-RoCBertForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RoCBertForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-RoCBertForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bec640115ad2e17050b19eff1707bdab07298dc60578eb5f5a0d81f81538051
- Size of remote file:
- 2.98 MB
- SHA256:
- 9465b7f9220a2ffec833935681786561ca724a38dbf9343658393c14536a5fb5
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.