Add bge-m3 and Nomic 1.5 embeddings. #1182
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nsthorat
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dsmilkov
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Feb 14, 2024
pyproject.toml
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| @@ -86,6 +89,7 @@ hdbscan = { version = "^0.8.33", optional = true } | |||
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| [tool.poetry.extras] | |||
| all = [ | |||
| "bge", | |||
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these have to be pypi packages, not aliases to groups
| embeddings = self._model.encode(doc, convert_to_tensor=True) | ||
| # Extract the dense vectors from the model. | ||
| embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],)) | ||
| embeddings = embeddings[:, : self._matryoshka_dim] |
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are dimensions sorted by importance , like PCA ? curious why we can just take the last 256/512 dimensions
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thats the magic of matryoshka_dim.. it's not PCA exactly, afaict it's baked into the loss function: https://aniketrege.github.io/blog/2024/mrl/
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Fixes #1148
I added two versions of the Nomic 1.5 embeddings, which use matryoshka_dim dims, see: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
Demo: https://lilacai-nikhil-staging.hf.space/datasets#local/glue_ax_map&schemaCollapsed=false