Instructions to use anudit/tag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use anudit/tag with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("anudit/tag") - sentence-transformers
How to use anudit/tag with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("anudit/tag") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- category_mappings.json +8 -8
- model.safetensors +1 -1
- pipeline.skops +1 -1
category_mappings.json
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"13": "space_exploration"
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},
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"valid_categories": [
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"intelligence",
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"space_exploration",
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"food_agri",
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"neuro_tech",
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"efficient_movement",
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"impossible_problems",
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"governance",
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"earth_rejuvination",
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"machines_robotics",
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"energy",
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"human_health",
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]
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}
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"13": "space_exploration"
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},
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"valid_categories": [
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"efficient_movement",
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"intelligence",
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"space_exploration",
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"earth_rejuvination",
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"food_agri",
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"secure_digital_world",
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"machines_robotics",
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"energy",
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"governance",
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"quantum",
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"human_health",
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"neuro_tech",
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"immersive",
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"impossible_problems"
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]
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}
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model.safetensors
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pipeline.skops
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size 7625703
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