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Trimmed EmbeddingGemma 300M for Haitian

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1_Pooling/config.json ADDED
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README.md ADDED
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+ ---
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+ pipeline_tag: fill-mask
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+ language: hat
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+ tags:
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+ - trimmed
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+ library_name: transformers
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+ base_model: google/embeddinggemma-300m
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+ base_model_relation: quantized
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+ datasets:
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+ - Lumberjackk/fineweb-2-trimming
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+ ---
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+
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+ # embeddinggemma-hat-16384
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+
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+ This model is a 62.3% smaller version of [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m)
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+ optimized for Haitian language via vocabulary trimming mined on [Lumberjackk/fineweb-2-trimming](https://huggingface.co/datasets/Lumberjackk/fineweb-2-trimming).
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+
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+ ## Model Statistics
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+ - **Original vocabulary size:** 262,144 tokens
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+ - **Trimmed vocabulary size:** 16,384 tokens
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+ - **Vocabulary reduction:** 93.7%
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+ - **Original model size:** 302,863,104 parameters
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+ - **Trimmed model size:** 114,119,424 parameters
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+ - **Size reduction:** 62.3%
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+
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+
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+ ## Usage
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("embeddinggemma-hat-16384")
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+
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+ # Run inference with queries and documents
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+ query = "My query"
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+ documents = [
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+ "Chunk 1",
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+ "Chunk 2",
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+ "Chunk 3",
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+ ]
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+ query_embeddings = model.encode_query(query)
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+ document_embeddings = model.encode_document(documents)
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+ print(query_embeddings.shape, document_embeddings.shape)
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+
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+ # Compute similarities to determine a ranking
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+ similarities = model.similarity(query_embeddings, document_embeddings)
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+ print(similarities)
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+ ```
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+ "_sliding_window_pattern": 6,
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+ "Gemma3TextModel"
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+ }
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+ "pytorch": "2.8.0+cu128"
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+ "prompts": {
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+ "document": "title: none | text: ",
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+ "BitextMining": "task: search result | query: ",
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+ "Clustering": "task: clustering | query: ",
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+ "Classification": "task: classification | query: ",
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+ "InstructionRetrieval": "task: code retrieval | query: ",
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+ "MultilabelClassification": "task: classification | query: ",
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+ "PairClassification": "task: sentence similarity | query: ",
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+ "Reranking": "task: search result | query: ",
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+ "Retrieval": "task: search result | query: ",
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+ "Retrieval-query": "task: search result | query: ",
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+ "Retrieval-document": "title: none | text: ",
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+ "STS": "task: sentence similarity | query: ",
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+ "Summarization": "task: summarization | query: "
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+ },
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+ "similarity_fn_name": "cosine"
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+ }
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