Instructions to use wim-uoc/spring-feather-content-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wim-uoc/spring-feather-content-transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="wim-uoc/spring-feather-content-transformer")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("wim-uoc/spring-feather-content-transformer") model = AutoModel.from_pretrained("wim-uoc/spring-feather-content-transformer", 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 sha256:4349ad9a2a7d0000809028ec7a359aac0efca7a7b9413c273166915f723e9a9c
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size 265462608
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