Sentence Similarity
sentence-transformers
Safetensors
embedding_gemma2
feature-extraction
dense
Generated from Trainer
dataset_size:3867
loss:CachedMultipleNegativesRankingLoss
loss:MultipleNegativesRankingLoss
Instructions to use Jbbok/my-embeddinggemma-2-sec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Jbbok/my-embeddinggemma-2-sec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jbbok/my-embeddinggemma-2-sec") sentences = [ "What was Honeywell International's other (income) expense in 2025?", "company: HONEYWELL INTERNATIONAL INC | ticker: HON | type: 10-K | report_date: 2025-12-31\n**Other (Income) Expense**\n\n### Other (income) expense\n\n- 2025: $(1,247)\n- 2024: $(843)\n- 2023: $(830)", "company: HONEYWELL INTERNATIONAL INC | ticker: HON | type: 10-K | report_date: 2025-12-31\n**INCOME BEFORE TAXES**\n\n### U.S.\n(us-gaap:IncomeLossFromContinuingOperationsBeforeIncomeTaxesDomestic)\n\n- 2025: $373 million [=373,000,000 USD]\n- 2024: $1,442 million [=1,442,000,000 USD]\n- 2023: $1,652 million [=1,652,000,000 USD]\n\n### Non-U.S.\n(us-gaap:IncomeLossFromContinuingOperationsBeforeIncomeTaxesForeign)\n\n- 2025: 5,103 million [=5,103,000,000 USD]\n- 2024: 4,802 million [=4,802,000,000 USD]\n- 2023: 4,539 million [=4,539,000,000 USD]\n\n### Total Income before taxes\n(us-gaap:IncomeLossFromContinuingOperationsBeforeIncomeTaxesExtraordinaryItemsNoncontrollingInterest)\n\n- 2025: $5,476 million [=5,476,000,000 USD]\n- 2024: $6,244 million [=6,244,000,000 USD]\n- 2023: $6,191 million [=6,191,000,000 USD]", "company: Mastercard Inc | ticker: MA | type: 10-K | report_date: 2025-12-31\nThe table below shows a summary of the cash flows from operating, investing and financing activities:\n\n*(unit: (in millions))*\n\n### Net cash provided by operating activities (For the Years Ended December 31,)\n\n- 2025: $17,648\n- 2024: $14,780\n- 2023: $11,980\n\n### Net cash used in investing activities (For the Years Ended December 31,)\n\n- 2025: (1,362)\n- 2024: (3,402)\n- 2023: (1,351)\n\n### Net cash used in financing activities (For the Years Ended December 31,)\n\n- 2025: (14,179)\n- 2024: (10,836)\n- 2023: (9,488)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 413 Bytes
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"name": "0",
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"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
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