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: 1,788 Bytes
623c829 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | {
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"audio_seq_length": 280,
"feature_extractor": {
"dither": 0.0,
"feature_extractor_type": "Gemma4AudioFeatureExtractor",
"feature_size": 128,
"fft_length": 512,
"fft_overdrive": false,
"frame_length": 320,
"hop_length": 160,
"input_scale_factor": 1.0,
"max_frequency": 8000.0,
"mel_floor": 0.001,
"min_frequency": 0.0,
"padding_side": "right",
"padding_value": 0.0,
"per_bin_mean": null,
"per_bin_stddev": null,
"preemphasis": 0.0,
"preemphasis_htk_flavor": true,
"return_attention_mask": true,
"sampling_rate": 16000
},
"image_processor": {
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"do_normalize": false,
"do_rescale": true,
"do_resize": true,
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],
"image_processor_type": "Gemma4ImageProcessor",
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"max_soft_tokens": 280,
"patch_size": 16,
"pooling_kernel_size": 3,
"resample": 3,
"rescale_factor": 0.00392156862745098
},
"image_seq_length": 280,
"processor_class": "EmbeddingGemma2Processor",
"video_processor": {
"add_timestamps": false,
"do_convert_rgb": true,
"do_normalize": false,
"do_rescale": true,
"do_resize": true,
"do_sample_frames": true,
"fps": 1,
"image_mean": [
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"patch_size": 16,
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"resample": 3,
"rescale_factor": 0.00392156862745098,
"return_metadata": false,
"video_processor_type": "EmbeddingGemma2VideoProcessor"
}
}
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