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Zen Embedding 2: EmbeddingGemma 2 (Google DeepMind, Apache-2.0) at 914f7f89, weights unchanged

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1_Pooling/config.json ADDED
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+ {
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+ "embedding_dimension": 768,
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+ {
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+ "module_input_name": "sentence_embedding",
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+ "module_output_name": "sentence_embedding"
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+ }
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+ ---
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+ license: apache-2.0
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+ base_model: google/embeddinggemma-2
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+ library_name: sentence-transformers
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+ pipeline_tag: feature-extraction
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+ language:
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+ - multilingual
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+ tags:
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+ - zen
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+ - zen-embedding
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+ - zenlm
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+ - hanzo
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+ - embedding
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+ - sentence-transformers
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+ - multimodal-embedding
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+ - vision
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+ - audio
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+ - video
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+ - retrieval
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+ - matryoshka
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+ ---
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+
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+ # Zen Embedding 2
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+
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+ One embedding space for text (including code), images, video and audio: any mix of them in, a
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+ 768-dimensional vector out, so a text query retrieves a screenshot, a clip or a code span directly.
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+ 8,192-token context (minutes of audio or video), Matryoshka truncation to 512, 256 or 128 dimensions,
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+ and encoders that load per modality: 270M parameters for text alone, 740M with vision and audio.
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+
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+ Repackaged from [google/embeddinggemma-2](https://huggingface.co/google/embeddinggemma-2) (Apache-2.0,
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+ Google DeepMind) at revision `914f7f89142e33e77833254d9c9b90c3cef7303b`, weights unchanged. **Not trained
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+ from scratch.** The upstream card has the benchmarks, training data and safety notes.
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+
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+ ## Usage
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+
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+ ```python
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+ import torch
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+ from sentence_transformers import SentenceTransformer
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+
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+ dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32
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+ model = SentenceTransformer("zenlm/zen-embedding-2", model_kwargs={"torch_dtype": dtype})
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+
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+ query = model.encode("task: code retrieval | query: where is the cache key built?")
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+ doc = model.encode("title: cache.py | text: def make_key(args, kwargs): ...")
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+ print(model.similarity(query, doc))
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+ ```
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+
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+ Run it in `bfloat16` or `float32`, never `float16`: its activations overflow `float16` into NaN.
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+
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+ Task prefixes steer the vector. Queries take `task: search result | query: …`,
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+ `task: question answering | query: …`, `task: fact checking | query: …` or
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+ `task: code retrieval | query: …`; documents take `title: {title or none} | text: …`; symmetric
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+ tasks put `task: classification | query: …`, `task: clustering | query: …` or
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+ `task: sentence similarity | query: …` on every input.
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+
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+ Mixed input marks each item's place with `<|image|>`, `<|video|>` or `<|audio|>`:
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+
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+ ```python
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+ emb = model.encode({
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+ "text": "Checkout page after submit: <|image|> Screen recording of the failure: <|video|>",
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+ "image": ["checkout.png"],
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+ "video": "failure.mp4",
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+ })
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+ ```
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+
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+ Text only, without the vision and audio encoders:
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+
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+ ```python
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+ model = SentenceTransformer("zenlm/zen-embedding-2",
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+ config_kwargs={"vision_config": None, "audio_config": None})
71
+ ```
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+
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+ Truncate with `model.encode(x, truncate_dim=256, normalize_embeddings=True)`; queries and documents
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+ must share a dimension.
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+
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+ Served on the Hanzo gateway (`api.hanzo.ai`) as `zen-embedding-2`.
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+
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+ ## License
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+
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+ `apache-2.0`. Upstream: **google/embeddinggemma-2** (EmbeddingGemma 2) by Google DeepMind. This repository
81
+ redistributes the weights unchanged under the same license.
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