davidmezzetti commited on
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Initial version

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Files changed (6) hide show
  1. .gitattributes +2 -0
  2. README.md +30 -0
  3. config.json +8 -0
  4. model.safetensors +3 -0
  5. model.sqlite +3 -0
  6. vocab.json +3 -0
.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ model.sqlite filter=lfs diff=lfs merge=lfs -text
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+ vocab.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ tags:
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+ - sentence-similarity
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+ inference: false
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+ license: pddl
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+ language: en
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+ library_name: staticvectors
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+ ---
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+
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+ # GloVe-6B StaticVectors model
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+
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+ This model is an export of these [GloVe-6B English Vectors](https://nlp.stanford.edu/projects/glove/) (_300d_) for [`staticvectors`](https://github.com/neuml/staticvectors). `staticvectors` enables running inference in Python with NumPy. This helps it maintain solid runtime performance.
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+
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+ ## Usage with StaticVectors
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+
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+ ```python
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+ from staticvectors import StaticVectors
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+
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+ model = StaticVectors("neuml/glove-6B")
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+ model.embeddings(["word"])
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+ ```
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+
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+ Given that pre-trained embeddings models can get quite large, there is also a SQLite version that lazily loads vectors.
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+
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+ ```python
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+ from staticvectors import StaticVectors
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+
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+ model = StaticVectors("neuml/glove-6B/model.sqlite")
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+ model.embeddings(["word"])
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+ ```
config.json ADDED
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+ {
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+ "model_type": "staticvectors",
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+ "storage": "safetensors",
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+ "format": "text",
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+ "source": "glove.6B.300d.txt",
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+ "total": 400000,
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+ "dim": 300
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+ }
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