Sentence Similarity
sentence-transformers
Safetensors
Afrikaans
gemma3_text
trimmed
text-embeddings-inference
Instructions to use alphaedge-ai/embeddinggemma-afr-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use alphaedge-ai/embeddinggemma-afr-32768 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("alphaedge-ai/embeddinggemma-afr-32768") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Upload 9 files
Browse files- 1_Pooling/config.json +10 -0
- 2_Dense/config.json +6 -0
- 2_Dense/model.safetensors +3 -0
- 3_Dense/config.json +6 -0
- 3_Dense/model.safetensors +3 -0
- config_sentence_transformers.json +26 -0
- generation_config.json +7 -0
- modules.json +32 -0
- sentence_bert_config.json +4 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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2_Dense/config.json
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{
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"in_features": 768,
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"out_features": 3072,
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"bias": false,
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"activation_function": "torch.nn.modules.linear.Identity"
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}
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2_Dense/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c327f2acb00149676ade24a75e11eb6ebbd367f9ee050267ba56829d2979f702
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size 9437272
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3_Dense/config.json
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{
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"in_features": 3072,
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"out_features": 768,
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"bias": false,
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"activation_function": "torch.nn.modules.linear.Identity"
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}
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3_Dense/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ffb6cc5162e11e2ce6bc2367e121ee3bbbc4e82e1ee26826bd7573d4948d81b8
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size 9437272
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config_sentence_transformers.json
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{
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"model_type": "SentenceTransformer",
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"__version__": {
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"sentence_transformers": "5.1.0",
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"transformers": "4.57.0.dev0",
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"pytorch": "2.8.0+cu128"
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},
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"prompts": {
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"query": "task: search result | query: ",
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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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"default_prompt_name": null,
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"similarity_fn_name": "cosine"
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}
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generation_config.json
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{
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"cache_implementation": "hybrid",
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"do_sample": true,
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"top_k": 64,
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"top_p": 0.95,
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"transformers_version": "4.57.0.dev0"
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}
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Dense",
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"type": "sentence_transformers.models.Dense"
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},
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{
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"idx": 3,
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"name": "3",
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"path": "3_Dense",
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"type": "sentence_transformers.models.Dense"
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},
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{
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"idx": 4,
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"name": "4",
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"path": "4_Normalize",
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"type": "sentence_transformers.models.Normalize"
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}
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]
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sentence_bert_config.json
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{
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"max_seq_length": 2048,
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"do_lower_case": false
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}
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