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
Download processor_config.json from Jbbok/my-embeddinggemma-2-sec: direct link, hf CLI and curl.
- Browser
- Download file 1.79 kB
-
https://huggingface.co/Jbbok/my-embeddinggemma-2-sec/resolve/main/processor_config.json
- Command line
-
hf download hf://Jbbok/my-embeddinggemma-2-sec/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/Jbbok/my-embeddinggemma-2-sec/resolve/main/processor_config.json
1.79 kB
| { | |
| "audio_ms_per_token": 40, | |
| "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": { | |
| "do_convert_rgb": true, | |
| "do_normalize": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_processor_type": "Gemma4ImageProcessor", | |
| "image_seq_length": 280, | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "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": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "max_frames": 32, | |
| "max_soft_tokens": 140, | |
| "overflow_strategy": "uniform", | |
| "patch_size": 16, | |
| "pooling_kernel_size": 3, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_metadata": false, | |
| "video_processor_type": "EmbeddingGemma2VideoProcessor" | |
| } | |
| } | |