Instructions to use hf-tiny-model-private/tiny-random-BartForQuestionAnswering 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-BartForQuestionAnswering 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-BartForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-BartForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-BartForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba6b7aad82826f66099c9f2d01ca2f21c7a65256c243a51e1a7ae0cc1d769e94
- Size of remote file:
- 138 kB
- SHA256:
- adc2bb70895050fec8c7e7eb9a7f16569fca16a230befd632ea1a6c4b5bc0a03
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.