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0xFarzad
/
embeddinggemma-graph-instructions-hard-negatives-final

Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:7786
loss:ContrastiveLossWithInBatchAndHardNegatives
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use 0xFarzad/embeddinggemma-graph-instructions-hard-negatives-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use 0xFarzad/embeddinggemma-graph-instructions-hard-negatives-final with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("0xFarzad/embeddinggemma-graph-instructions-hard-negatives-final")
    
    sentences = [
        "<ROUTE_START>\n<SEG> ST_1 -> ST_2 <DIR:RIGHT> <LIGHT> <POI: Pie Face / amenity: cafe / cuisine: coffee_shop>\n<SEG> ST_2 -> ST_3 <DIR:STRAIGHT> <POI_RIGHT: amenity: parking> <POI_LEFT: Zoob Zib Aura Thai / amenity: restaurant / cuisine: thai> <POI_LEFT: Pie Face / amenity: cafe / cuisine: coffee_shop> <POI_LEFT: Burgers and Cupcakes / amenity: restaurant / cuisine: burger>\n<SEG> ST_3 -> ST_4 <DIR:STRAIGHT> <POI_RIGHT: amenity: parking> <POI_LEFT: Zoob Zib Aura Thai / amenity: restaurant / cuisine: thai> <POI_LEFT: Burgers and Cupcakes / amenity: restaurant / cuisine: burger> <POI_LEFT: Sergimmo Slumeria / amenity: restaurant / cuisine: italian>\n<SEG> ST_4 -> ST_5 <DIR:STRAIGHT> <POI_RIGHT: amenity: parking> <POI_LEFT: Burgers and Cupcakes / amenity: restaurant / cuisine: burger> <POI_LEFT: Sergimmo Slumeria / amenity: restaurant / cuisine: italian> <POI_LEFT: shop: convenience>\n<SEG> ST_5 -> ST_6 <DIR:STRAIGHT> <LIGHT> <POI_LEFT: Golden City / amenity: restaurant> <POI_LEFT: Deli & Pizzeria / amenity: fast_food / cuisine: pizza>\n<SEG> ST_6 -> ST_7 <DIR:STRAIGHT> <POI_LEFT: Hudson Station Bar and Grill / amenity: restaurant / cuisine: steak_house> <POI_LEFT: Uncle Jack's / amenity: restaurant / cuisine: steak_house>\n<SEG> ST_7 -> ST_8 <DIR:STRAIGHT> <POI_RIGHT: Café Bistro / amenity: fast_food / cuisine: deli,_buffet,_Asian,_salad,_pizza>\n<SEG> ST_8 -> ST_9 <DIR:STRAIGHT> <POI_RIGHT: Café Bistro / amenity: fast_food / cuisine: deli,_buffet,_Asian,_salad,_pizza> <POI_LEFT: Five Guys / amenity: fast_food>\n<ROUTE_END>",
        "Walk to the light where Pie Face may be on the corner and turn right. Pass a parking lot on the right and go through a light. At the following light where Golden City and Deli & Pizzeria may be on the corners turn left. Stop 1/2 down the block next to Cafe Bistro just before Five Guys on the right side.",
        "Go straight past M408 Performing Arts high school then at the intersection where Chaan Teng and take a left.",
        "Walk to the light where Pie Face may be on the corner and turn right. Pass a parking lot on the right and go through a light. At the following light where Golden City and Deli & Pizzeria may be on the corners turn left. Stop 1/2 down the block next to Cafe Bistro just before Five Guys on the right side."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
embeddinggemma-graph-instructions-hard-negatives-final / 1_Pooling
312 Bytes
Ctrl+K
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  • 1 contributor
History: 1 commit
0xFarzad's picture
0xFarzad
Add new SentenceTransformer model
2a2fb28 verified 11 months ago
  • config.json
    312 Bytes
    Add new SentenceTransformer model 11 months ago