Feature Extraction
Transformers
Safetensors
Spanish
English
toarim
sentence-similarity
stateful-embeddings
nested-learning
rag
industrial-rag
cpu-optimized
sherman-morrison
low-rank-svd
custom_code
Instructions to use ManyCamDS/ToarimEmbedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManyCamDS/ToarimEmbedder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ManyCamDS/ToarimEmbedder", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ManyCamDS/ToarimEmbedder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 241 Bytes
ce6a427 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"architectures": [
"T\u00f3arimEmbeddingModel"
],
"model_type": "toarim",
"vocab_size": 30522,
"embed_dim": 768,
"latent_k": 128,
"auto_map": {
"AutoModel": "toarim_embedder.T\u00f3arimEmbeddingModel"
}
} |