Instructions to use vaibhav9/custom-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaibhav9/custom-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vaibhav9/custom-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vaibhav9/custom-roberta") model = AutoModelForQuestionAnswering.from_pretrained("vaibhav9/custom-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:cdc2c6d3d3dd963d0e26503469a3a02373f109e278d0d679006500bf02b13b88
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size 326133904
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