Text Classification
Transformers
Safetensors
English
llama
text-generation
content-moderation
safety
text-embeddings-inference
Instructions to use UnionStreet/VISION-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UnionStreet/VISION-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UnionStreet/VISION-1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UnionStreet/VISION-1") model = AutoModelForCausalLM.from_pretrained("UnionStreet/VISION-1") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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# VISION-1: Content Safety Analysis Model
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VISION-1 is a fine-tuned version of Llama 3.1 8B Instruct, specialized for content safety analysis and moderation. The model is trained to identify and analyze potential safety concerns in text content, including scams, fraud, harmful content, and inappropriate material.
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---
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language:
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- en
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tags:
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- text-classification
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- content-moderation
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- safety
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- transformers
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pipeline_tag: text-classification
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license: llama2
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datasets:
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- OverseerAI/safety-content
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base_model: meta-llama/Llama-3.1-8B-Instruct
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library_name: transformers
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---
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# VISION-1: Content Safety Analysis Model
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VISION-1 is a fine-tuned version of Llama 3.1 8B Instruct, specialized for content safety analysis and moderation. The model is trained to identify and analyze potential safety concerns in text content, including scams, fraud, harmful content, and inappropriate material.
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