Instructions to use dpv/roberta-minipile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dpv/roberta-minipile with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dpv/roberta-minipile")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dpv/roberta-minipile") model = AutoModel.from_pretrained("dpv/roberta-minipile") - 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 sha256:302ec841a80ffa80c684b8d4d6eba8d64d414a97e7efbbb5f278d6f5d7a387b8
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size 498606016
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