Text Classification
Transformers
Safetensors
llama
text-generation
brand-safety
content-moderation
apple-silicon
metal
mps
Eval Results (legacy)
text-embeddings-inference
Instructions to use UnionStreet/vision-1-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UnionStreet/vision-1-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UnionStreet/vision-1-mini")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UnionStreet/vision-1-mini") model = AutoModelForCausalLM.from_pretrained("UnionStreet/vision-1-mini") - Notebooks
- Google Colab
- Kaggle
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@@ -156,7 +156,7 @@ If you use this model in your research, please cite:
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@misc{vision-1-mini,
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author = {Max Sonderby},
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title = {Vision-1-Mini: Optimized Brand Safety Classification Model},
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year = {
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publisher = {Hugging Face},
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journal = {Hugging Face Model Hub},
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howpublished = {\url{https://huggingface.co/maxsonderby/vision-1-mini}}
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@misc{vision-1-mini,
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author = {Max Sonderby},
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title = {Vision-1-Mini: Optimized Brand Safety Classification Model},
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year = {2025},
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publisher = {Hugging Face},
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journal = {Hugging Face Model Hub},
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howpublished = {\url{https://huggingface.co/maxsonderby/vision-1-mini}}
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