Instructions to use hf-tiny-model-private/tiny-random-RobertaPreLayerNormForQuestionAnswering 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-RobertaPreLayerNormForQuestionAnswering 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-RobertaPreLayerNormForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RobertaPreLayerNormForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-RobertaPreLayerNormForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba50446d0a7ca3813b830e24fa01b7f1cc61e2aae77258f8d1214ee7d2e6a070
- Size of remote file:
- 350 kB
- SHA256:
- 96faa560769855420b8f95930a9b526b463d1add6b6c038e4cb4fc256a2c0acb
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.