Instructions to use hf-tiny-model-private/tiny-random-MegatronBertForQuestionAnswering 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-MegatronBertForQuestionAnswering 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-MegatronBertForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MegatronBertForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-MegatronBertForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18540bd8b4f34969f04a5aae91724d3e2a548c58ae6a5fe7f0a03200c6866727
- Size of remote file:
- 890 kB
- SHA256:
- b3f53f3981c1679a56584794eb5ea9e3cbebf7a5171f9457623db97d2962d1d7
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.