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 tokenizer_config.json from Jbbok/my-embeddinggemma-2-sec: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
-
https://huggingface.co/Jbbok/my-embeddinggemma-2-sec/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Jbbok/my-embeddinggemma-2-sec/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Jbbok/my-embeddinggemma-2-sec/resolve/main/tokenizer_config.json
1.57 kB
| { | |
| "audio_token": "<|audio|>", | |
| "backend": "tokenizers", | |
| "boa_token": "<|audio>", | |
| "boi_token": "<|image>", | |
| "bos_token": "<bos>", | |
| "eoa_token": "<audio|>", | |
| "eoc_token": "<channel|>", | |
| "eoi_token": "<image|>", | |
| "eos_token": "<eos>", | |
| "eot_token": "<turn|>", | |
| "escape_token": "<|\"|>", | |
| "etc_token": "<tool_call|>", | |
| "etd_token": "<tool|>", | |
| "etr_token": "<tool_response|>", | |
| "extra_special_tokens": [ | |
| "<|video|>" | |
| ], | |
| "image_token": "<|image|>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 4096, | |
| "model_specific_special_tokens": { | |
| "audio_token": "<|audio|>", | |
| "boa_token": "<|audio>", | |
| "boi_token": "<|image>", | |
| "eoa_token": "<audio|>", | |
| "eoc_token": "<channel|>", | |
| "eoi_token": "<image|>", | |
| "eot_token": "<turn|>", | |
| "escape_token": "<|\"|>", | |
| "etc_token": "<tool_call|>", | |
| "etd_token": "<tool|>", | |
| "etr_token": "<tool_response|>", | |
| "image_token": "<|image|>", | |
| "soc_token": "<|channel>", | |
| "sot_token": "<|turn>", | |
| "stc_token": "<|tool_call>", | |
| "std_token": "<|tool>", | |
| "str_token": "<|tool_response>", | |
| "think_token": "<|think|>", | |
| "video_token": "<|video|>" | |
| }, | |
| "pad_token": "<pad>", | |
| "padding_side": "right", | |
| "processor_class": "EmbeddingGemma2Processor", | |
| "soc_token": "<|channel>", | |
| "sot_token": "<|turn>", | |
| "stc_token": "<|tool_call>", | |
| "std_token": "<|tool>", | |
| "str_token": "<|tool_response>", | |
| "think_token": "<|think|>", | |
| "tokenizer_class": "GemmaTokenizer", | |
| "unk_token": "<unk>", | |
| "video_token": "<|video|>" | |
| } | |