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:
- aaf7c932e9855b63140b9c2f6a79ae0fe30a05e13aaac6dc2eea6e1ea811fc4f
- Size of remote file:
- 2.96 MB
- SHA256:
- ffed0252a1f2d4a71c6112caa6583e5a25956c1f0ba6dd5411bff1b9773b00df
路
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