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.gitattributes

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README.md

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+---
+base_model: unknown
+library_name: model2vec
+license: mit
+model_name: category_classifier
+tags:
+- embeddings
+- static-embeddings
+- sentence-transformers
+---
+
+# category_classifier Model Card
+
+This [Model2Vec](https://github.com/MinishLab/model2vec) model is a fine-tuned version of the [unknown](https://huggingface.co/unknown) Model2Vec model. It also includes a classifier head on top.
+
+## Installation
+
+Install model2vec using pip:
+```
+pip install model2vec[inference]
+```
+
+## Usage
+Load this model using the `from_pretrained` method:
+```python
+from model2vec.inference import StaticModelPipeline
+
+# Load a pretrained Model2Vec model
+model = StaticModelPipeline.from_pretrained("category_classifier")
+
+# Predict labels
+predicted = model.predict(["Example sentence"])
+```
+
+## Additional Resources
+
+- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
+- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
+- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
+- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
+- [Website](https://minishlab.github.io/)
+
+## Library Authors
+
+Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
+
+## Citation
+
+Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
+```
+@article{minishlab2024model2vec,
+  author = {Tulkens, Stephan and {van Dongen}, Thomas},
+  title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
+  year = {2024},
+  url = {https://github.com/MinishLab/model2vec}
+}
+```

category_mappings.json

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+{
+  "category_to_idx": {
+    "earth_rejuvination": 0,
+    "efficient_movement": 1,
+    "energy": 2,
+    "food_agri": 3,
+    "governance": 4,
+    "human_health": 5,
+    "immersive": 6,
+    "impossible_problems": 7,
+    "intelligence": 8,
+    "machines_robotics": 9,
+    "neuro_tech": 10,
+    "quantum": 11,
+    "secure_digital_world": 12,
+    "space_exploration": 13
+  },
+  "idx_to_category": {
+    "0": "earth_rejuvination",
+    "1": "efficient_movement",
+    "2": "energy",
+    "3": "food_agri",
+    "4": "governance",
+    "5": "human_health",
+    "6": "immersive",
+    "7": "impossible_problems",
+    "8": "intelligence",
+    "9": "machines_robotics",
+    "10": "neuro_tech",
+    "11": "quantum",
+    "12": "secure_digital_world",
+    "13": "space_exploration"
+  },
+  "valid_categories": [
+    "neuro_tech",
+    "energy",
+    "secure_digital_world",
+    "earth_rejuvination",
+    "impossible_problems",
+    "intelligence",
+    "space_exploration",
+    "efficient_movement",
+    "quantum",
+    "human_health",
+    "governance",
+    "immersive",
+    "machines_robotics",
+    "food_agri"
+  ]
+}

config.json

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+{
+    "normalize": true,
+    "embedding_dtype": "float32",
+    "vocabulary_quantization": 63091
+}

model.safetensors

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modules.json

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+[
+    {
+        "idx": 0,
+        "name": "0",
+        "path": ".",
+        "type": "sentence_transformers.models.StaticEmbedding"
+    },
+    {
+        "idx": 1,
+        "name": "1",
+        "path": "1_Normalize",
+        "type": "sentence_transformers.models.Normalize"
+    }
+]

pipeline.skops

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