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 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

2_Dense/config.json

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+    "in_features": 768,
+    "out_features": 3072,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

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3_Dense/config.json

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+    "in_features": 3072,
+    "out_features": 768,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

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README.md

@@ -0,0 +1,1075 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:1200
+- loss:ContrastiveLoss
+base_model: google/embeddinggemma-300m
+widget:
+- source_sentence: 'TaxYear: 2025 PRELIMINARY
+
+    LandJustValue: $571,965
+
+    ImprovementsJustValue: $444,893
+
+    TotalJustValue: $1,016,858
+
+    SchoolAssessedValue: $657,902
+
+    CountyTaxableValue: $607,180
+
+    TotalTaxes: $5,881.02
+
+
+    TaxYear: 2024
+
+    LandJustValue: $529,037
+
+    ImprovementsJustValue: $522,202
+
+    TotalJustValue: $1,051,239
+
+    SchoolAssessedValue: $639,361
+
+    CountyTaxableValue: $589,361
+
+    TotalTaxes: $6,003.53
+
+
+    TaxYear: 2023
+
+    LandJustValue: $500,470
+
+    ImprovementsJustValue: $572,889
+
+    TotalJustValue: $1,073,359
+
+    SchoolAssessedValue: $620,739
+
+    CountyTaxableValue: $570,739
+
+    TotalTaxes: $5,956.52
+
+
+    TaxYear: 2022
+
+    LandJustValue: $230,519
+
+    ImprovementsJustValue: $610,503
+
+    TotalJustValue: $841,022
+
+    SchoolAssessedValue: $602,659
+
+    CountyTaxableValue: $552,659
+
+    TotalTaxes: $6,124.86
+
+
+    TaxYear: 2021
+
+    LandJustValue: $112,658
+
+    ImprovementsJustValue: $472,448
+
+    TotalJustValue: $585,106
+
+    SchoolAssessedValue: $585,106
+
+    CountyTaxableValue: $535,106
+
+    TotalTaxes: $6,190.98section: Tax,
+
+    county: Collier,
+
+    parcel_id: 82660002628'
+  sentences:
+  - 'area_under_air: 2111
+
+    livable_floor_area: 2111
+
+    parcel_identifier: 51978031927
+
+    property_structure_built_year: 2004
+
+    property_type: SingleFamily
+
+    subdivision: INDIGO LAKES UNIT
+
+    total_area: 2551'
+  - 'monthly_tax_amount: 490.09
+
+    period_end_date: 2025-12-31
+
+    period_start_date: 2025-01-01
+
+    property_assessed_value_amount: 657902
+
+    property_building_amount: 444893
+
+    property_land_amount: 571965
+
+    property_market_value_amount: 1016858
+
+    property_taxable_value_amount: 607180
+
+    tax_year: 2025
+
+    yearly_tax_amount: 5881.02
+
+
+    monthly_tax_amount: 510.41
+
+    period_end_date: 2022-12-31
+
+    period_start_date: 2022-01-01
+
+    property_assessed_value_amount: 602659
+
+    property_building_amount: 610503
+
+    property_land_amount: 230519
+
+    property_market_value_amount: 841022
+
+    property_taxable_value_amount: 552659
+
+    tax_year: 2022
+
+    yearly_tax_amount: 6124.86'
+  - 'ownership_transfer_date: 2013-07-09
+
+    purchase_price_amount: 830000
+
+
+    ownership_transfer_date: 2011-10-03
+
+    purchase_price_amount: 685000
+
+
+    ownership_transfer_date: 2009-07-01
+
+    purchase_price_amount: 432500
+
+
+    ownership_transfer_date: 1999-02-22
+
+    purchase_price_amount: 0
+
+
+    ownership_transfer_date: 2001-01-25
+
+    purchase_price_amount: 360000'
+- source_sentence: 'TaxYear: 2025 PRELIMINARY
+
+    LandJustValue: $0
+
+    ImprovementsJustValue: $261,720
+
+    TotalJustValue: $261,720
+
+    SchoolAssessedValue: $261,720
+
+    CountyTaxableValue: $261,720
+
+    TotalTaxes: $3,142.17
+
+
+    TaxYear: 2024
+
+    LandJustValue: $0
+
+    ImprovementsJustValue: $261,720
+
+    TotalJustValue: $261,720
+
+    SchoolAssessedValue: $261,720
+
+    CountyTaxableValue: $261,720
+
+    TotalTaxes: $3,551.55
+
+
+    TaxYear: 2023
+
+    LandJustValue: $0
+
+    ImprovementsJustValue: $298,680
+
+    TotalJustValue: $298,680
+
+    SchoolAssessedValue: $298,680
+
+    CountyTaxableValue: $298,680
+
+    TotalTaxes: $4,125.27
+
+
+    TaxYear: 2022
+
+    LandJustValue: $0
+
+    ImprovementsJustValue: $233,985
+
+    TotalJustValue: $233,985
+
+    SchoolAssessedValue: $233,985
+
+    CountyTaxableValue: $172,700
+
+    TotalTaxes: $2,771.07
+
+
+    TaxYear: 2021
+
+    LandJustValue: $0
+
+    ImprovementsJustValue: $157,000
+
+    TotalJustValue: $157,000
+
+    SchoolAssessedValue: $157,000
+
+    CountyTaxableValue: $157,000
+
+    TotalTaxes: $2,342.18section: Tax,
+
+    county: Collier,
+
+    parcel_id: 31760000209'
+  sentences:
+  - 'first_name: George
+
+    last_name: Lewis
+
+    middle_name: P
+
+
+    first_name: Karen
+
+    last_name: Lewis
+
+    middle_name: L'
+  - 'area_under_air: 997
+
+    livable_floor_area: 997
+
+    parcel_identifier: 31731720000
+
+    property_legal_description_text: FAIRWAY FOREST GARDEN VILLAS A CONDOMINIUM UNIT
+    179
+
+    property_structure_built_year: 1987
+
+    property_type: Condominium
+
+    total_area: 997'
+  - 'monthly_tax_amount: 195.18
+
+    period_end_date: 2021-12-31
+
+    period_start_date: 2021-01-01
+
+    property_assessed_value_amount: 157000
+
+    property_building_amount: 157000
+
+    property_land_amount: 0
+
+    property_market_value_amount: 157000
+
+    property_taxable_value_amount: 157000
+
+    tax_year: 2021
+
+    yearly_tax_amount: 2342.18
+
+
+    monthly_tax_amount: 261.85
+
+    period_end_date: 2025-12-31
+
+    period_start_date: 2025-01-01
+
+    property_assessed_value_amount: 261720
+
+    property_building_amount: 261720
+
+    property_land_amount: 0
+
+    property_market_value_amount: 261720
+
+    property_taxable_value_amount: 261720
+
+    tax_year: 2025
+
+    yearly_tax_amount: 3142.17
+
+
+    monthly_tax_amount: 295.96
+
+    period_end_date: 2024-12-31
+
+    period_start_date: 2024-01-01
+
+    property_assessed_value_amount: 261720
+
+    property_building_amount: 261720
+
+    property_land_amount: 0
+
+    property_market_value_amount: 261720
+
+    property_taxable_value_amount: 261720
+
+    tax_year: 2024
+
+    yearly_tax_amount: 3551.55
+
+
+    monthly_tax_amount: 230.92
+
+    period_end_date: 2022-12-31
+
+    period_start_date: 2022-01-01
+
+    property_assessed_value_amount: 233985
+
+    property_building_amount: 233985
+
+    property_land_amount: 0
+
+    property_market_value_amount: 233985
+
+    property_taxable_value_amount: 172700
+
+    tax_year: 2022
+
+    yearly_tax_amount: 2771.07
+
+
+    monthly_tax_amount: 343.77
+
+    period_end_date: 2023-12-31
+
+    period_start_date: 2023-01-01
+
+    property_assessed_value_amount: 298680
+
+    property_building_amount: 298680
+
+    property_land_amount: 0
+
+    property_market_value_amount: 298680
+
+    property_taxable_value_amount: 298680
+
+    tax_year: 2023
+
+    yearly_tax_amount: 4125.27'
+- source_sentence: 'ParcelID: 31347702043
+
+    FullAddress: 9424 MONTELANICO LOOP, NAPLES 34119
+
+    Legal: ESPLANADE GOLF AND COUNTRY CLUB OF NAPLES PHASE 3 BLOCKS K1 K2 AND H3 LOT
+    1390
+
+    Subdivision: 281740 - ESPLANADE G&CC PH3 B-K1,K2,H3 CLUB OF NAPLES PHASE 3 BLOCKS
+    K1 K2 AND H3
+
+    UseCode: 1 - SINGLE FAMILY RESIDENTIAL
+
+    Section: 15
+
+    Township: 48
+
+    Range: 26section: Property,
+
+    county: Collier,
+
+    parcel_id: 31347702043'
+  sentences:
+  - 'monthly_tax_amount: 1296.8
+
+    period_end_date: 2023-12-31
+
+    period_start_date: 2023-01-01
+
+    property_assessed_value_amount: 1452003
+
+    property_building_amount: 1459158
+
+    property_land_amount: 1594430
+
+    property_market_value_amount: 3053588
+
+    property_taxable_value_amount: 1402003
+
+    tax_year: 2023
+
+    yearly_tax_amount: 15561.55
+
+
+    monthly_tax_amount: 1339.02
+
+    period_end_date: 2021-12-31
+
+    period_start_date: 2021-01-01
+
+    property_assessed_value_amount: 1368652
+
+    property_building_amount: 1188323
+
+    property_land_amount: 180329
+
+    property_market_value_amount: 1368652
+
+    property_taxable_value_amount: 1318652
+
+    tax_year: 2021
+
+    yearly_tax_amount: 16068.19
+
+
+    monthly_tax_amount: 1315.87
+
+    period_end_date: 2024-12-31
+
+    period_start_date: 2024-01-01
+
+    property_assessed_value_amount: 1495563
+
+    property_building_amount: 1262216
+
+    property_land_amount: 1402668
+
+    property_market_value_amount: 2664884
+
+    property_taxable_value_amount: 1445563
+
+    tax_year: 2024
+
+    yearly_tax_amount: 15790.39
+
+
+    monthly_tax_amount: 1187.99
+
+    period_end_date: 2025-12-31
+
+    period_start_date: 2025-01-01
+
+    property_assessed_value_amount: 1538934
+
+    property_building_amount: 1117620
+
+    property_land_amount: 1508245
+
+    property_market_value_amount: 2625865
+
+    property_taxable_value_amount: 1488212
+
+    tax_year: 2025
+
+    yearly_tax_amount: 14255.93
+
+
+    monthly_tax_amount: 1334.85
+
+    period_end_date: 2022-12-31
+
+    period_start_date: 2022-01-01
+
+    property_assessed_value_amount: 1409712
+
+    property_building_amount: 1553410
+
+    property_land_amount: 470644
+
+    property_market_value_amount: 2024054
+
+    property_taxable_value_amount: 1359712
+
+    tax_year: 2022
+
+    yearly_tax_amount: 16018.16'
+  - 'area_under_air: 2313
+
+    livable_floor_area: 2313
+
+    parcel_identifier: 31347702043
+
+    property_legal_description_text: ESPLANADE GOLF AND COUNTRY CLUB OF NAPLES PHASE
+    3 BLOCKS K1 K2 AND H3 LOT 1390
+
+    property_structure_built_year: 2018
+
+    property_type: SingleFamily
+
+    subdivision: ESPLANADE G&CC PH3 B-K1,K2,H3 CLUB OF NAPLES PHASE 3 BLOCKS K1 K2
+    AND H3
+
+    total_area: 2767'
+  - 'city_name: NAPLES
+
+    postal_code: 34105
+
+    range: 25
+
+    section: 14
+
+    state_code: FL
+
+    street_name: WOODSHIRE
+
+    street_number: 1018
+
+    street_suffix_type: Ln
+
+    township: 49'
+- source_sentence: 'OwnerLine 1: 21 VB PROPERTIES LLCsection: Owners,
+
+    county: Collier,
+
+    parcel_id: 23270120001'
+  sentences:
+  - 'first_name: Kenneth
+
+    last_name: Holman
+
+    middle_name: W'
+  - 'city_name: NAPLES
+
+    state_code: FL
+
+    street_name: WILLOWBROOK
+
+    street_number: 765
+
+    street_suffix_type: Dr
+
+    township: 49'
+  - 'name: 21'
+- source_sentence: 'FullAddress: 5852 NORTHRIDGE DR, NAPLES 34110
+
+    Legal: CARLTON LAKES UNIT NO 2 BLK A LOT 5 NKA VILLAS I AT CARLTON LAKES (HO)
+    UNIT A-5
+
+    Section: 19
+
+    Township: 48
+
+    Range: 26section: Address,
+
+    county: Collier,
+
+    parcel_id: 25540003380'
+  sentences:
+  - 'monthly_tax_amount: 317.4
+
+    period_end_date: 2022-12-31
+
+    period_start_date: 2022-01-01
+
+    property_assessed_value_amount: 381299
+
+    property_building_amount: 441115
+
+    property_land_amount: 134469
+
+    property_market_value_amount: 575584
+
+    property_taxable_value_amount: 331299
+
+    tax_year: 2022
+
+    yearly_tax_amount: 3808.76
+
+
+    monthly_tax_amount: 517.39
+
+    period_end_date: 2025-12-31
+
+    period_start_date: 2025-01-01
+
+    property_assessed_value_amount: 692367
+
+    property_building_amount: 324162
+
+    property_land_amount: 368205
+
+    property_market_value_amount: 692367
+
+    property_taxable_value_amount: 641645
+
+    tax_year: 2025
+
+    yearly_tax_amount: 6208.64
+
+
+    monthly_tax_amount: 320.37
+
+    period_end_date: 2021-12-31
+
+    period_start_date: 2021-01-01
+
+    property_assessed_value_amount: 370193
+
+    property_building_amount: 334803
+
+    property_land_amount: 35390
+
+    property_market_value_amount: 370193
+
+    property_taxable_value_amount: 320193
+
+    tax_year: 2021
+
+    yearly_tax_amount: 3844.46'
+  - 'first_name: Christina
+
+    last_name: Zajac
+
+    middle_name: R
+
+
+    first_name: Thomas
+
+    last_name: Zajac
+
+    middle_name: H'
+  - 'city_name: NAPLES
+
+    lot: 5
+
+    postal_code: 34110
+
+    range: 26
+
+    section: 19
+
+    state_code: FL
+
+    street_name: NORTHRIDGE
+
+    street_number: 5852
+
+    street_suffix_type: Dr
+
+    township: 48'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- cosine_mcc
+model-index:
+- name: EmbeddingGemma-300m trained to measure coverage
+  results:
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy
+      value: 0.96
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.9616374969482422
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.961038961038961
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.9616374969482422
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.9367088607594937
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.9866666666666667
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.9525238269081858
+      name: Cosine Ap
+    - type: cosine_mcc
+      value: 0.9213112424256316
+      name: Cosine Mcc
+---
+
+# EmbeddingGemma-300m trained to measure coverage
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
+- **Maximum Sequence Length:** 2048 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+  (4): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("mancer146/embeddinggemma-300m-haystack-contrastive-better-thin")
+# Run inference
+queries = [
+    "FullAddress: 5852 NORTHRIDGE DR, NAPLES 34110\nLegal: CARLTON LAKES UNIT NO 2 BLK A LOT 5 NKA VILLAS I AT CARLTON LAKES (HO) UNIT A-5\nSection: 19\nTownship: 48\nRange: 26section: Address,\ncounty: Collier,\nparcel_id: 25540003380",
+]
+documents = [
+    'city_name: NAPLES\nlot: 5\npostal_code: 34110\nrange: 26\nsection: 19\nstate_code: FL\nstreet_name: NORTHRIDGE\nstreet_number: 5852\nstreet_suffix_type: Dr\ntownship: 48',
+    'monthly_tax_amount: 317.4\nperiod_end_date: 2022-12-31\nperiod_start_date: 2022-01-01\nproperty_assessed_value_amount: 381299\nproperty_building_amount: 441115\nproperty_land_amount: 134469\nproperty_market_value_amount: 575584\nproperty_taxable_value_amount: 331299\ntax_year: 2022\nyearly_tax_amount: 3808.76\n\nmonthly_tax_amount: 517.39\nperiod_end_date: 2025-12-31\nperiod_start_date: 2025-01-01\nproperty_assessed_value_amount: 692367\nproperty_building_amount: 324162\nproperty_land_amount: 368205\nproperty_market_value_amount: 692367\nproperty_taxable_value_amount: 641645\ntax_year: 2025\nyearly_tax_amount: 6208.64\n\nmonthly_tax_amount: 320.37\nperiod_end_date: 2021-12-31\nperiod_start_date: 2021-01-01\nproperty_assessed_value_amount: 370193\nproperty_building_amount: 334803\nproperty_land_amount: 35390\nproperty_market_value_amount: 370193\nproperty_taxable_value_amount: 320193\ntax_year: 2021\nyearly_tax_amount: 3844.46',
+    'first_name: Christina\nlast_name: Zajac\nmiddle_name: R\n\nfirst_name: Thomas\nlast_name: Zajac\nmiddle_name: H',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 768] [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[0.9780, 0.8260, 0.6371]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Binary Classification
+
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                    | Value      |
+|:--------------------------|:-----------|
+| cosine_accuracy           | 0.96       |
+| cosine_accuracy_threshold | 0.9616     |
+| cosine_f1                 | 0.961      |
+| cosine_f1_threshold       | 0.9616     |
+| cosine_precision          | 0.9367     |
+| cosine_recall             | 0.9867     |
+| **cosine_ap**             | **0.9525** |
+| cosine_mcc                | 0.9213     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### json
+
+* Dataset: json
+* Size: 1,200 training samples
+* Columns: <code>input_text</code>, <code>output_text</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | input_text                                                                           | output_text                                                                         | label                                           |
+  |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                               | string                                                                              | int                                             |
+  | details | <ul><li>min: 36 tokens</li><li>mean: 188.79 tokens</li><li>max: 536 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 164.22 tokens</li><li>max: 801 tokens</li></ul> | <ul><li>0: ~50.10%</li><li>1: ~49.90%</li></ul> |
+* Samples:
+  | input_text                                                                                                                         | output_text                                                                  | label          |
+  |:-----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:---------------|
+  | <code>OwnerLine 1: JERI HURCKES LIVING TRUSTsection: Owners,<br>county: Collier,<br>parcel_id: 82660021104</code>                  | <code>name: JERI HURCKES LIVING TRUST</code>                                 | <code>1</code> |
+  | <code>OwnerLine 1: GUALARIO, ANTHONY=& DIANAsection: Owners,<br>county: Collier,<br>parcel_id: 16054320005</code>                  | <code>first_name: Anthony<br>last_name: Gualario</code>                      | <code>0</code> |
+  | <code>Date: 02/11/14<br>Amount: $496,300<br>BookPage: 5009-963section: Sales,<br>county: Collier,<br>parcel_id: 69770005923</code> | <code>ownership_transfer_date: 2014-02-11<br>purchase_price_amount: 0</code> | <code>0</code> |
+* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
+      "margin": 0.1,
+      "size_average": true
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 3
+- `per_device_eval_batch_size`: 3
+- `gradient_accumulation_steps`: 2
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.05
+- `fp16`: True
+- `prompts`: {'input_text': 'STS', 'output_text': 'STS'}
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 3
+- `per_device_eval_batch_size`: 3
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: {'input_text': 'STS', 'output_text': 'STS'}
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch | Step | cosine_ap |
+|:-----:|:----:|:---------:|
+| 0.4   | 40   | 0.8756    |
+| 0.8   | 80   | 0.8939    |
+| 1.2   | 120  | 0.8644    |
+| 1.6   | 160  | 0.9116    |
+| 2.0   | 200  | 0.9696    |
+| 2.4   | 240  | 0.9559    |
+| 2.8   | 280  | 0.9412    |
+| 3.2   | 320  | 0.9567    |
+| 3.6   | 360  | 0.9543    |
+| 4.0   | 400  | 0.9525    |
+
+
+### Framework Versions
+- Python: 3.11.13
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.0.dev0
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.9.0
+- Datasets: 4.1.1
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### ContrastiveLoss
+```bibtex
+@inproceedings{hadsell2006dimensionality,
+    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
+    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
+    title={Dimensionality Reduction by Learning an Invariant Mapping},
+    year={2006},
+    volume={2},
+    number={},
+    pages={1735-1742},
+    doi={10.1109/CVPR.2006.100}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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+}

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3TextModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "dtype": "float32",
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+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_types": [
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+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 2048,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 3,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 129,
+  "transformers_version": "4.57.0.dev0",
+  "use_bidirectional_attention": true,
+  "use_cache": true,
+  "vocab_size": 262144
+}

config_sentence_transformers.json

@@ -0,0 +1,26 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.57.0.dev0",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "task: search result | query: ",
+    "document": "title: none | text: ",
+    "BitextMining": "task: search result | query: ",
+    "Clustering": "task: clustering | query: ",
+    "Classification": "task: classification | query: ",
+    "InstructionRetrieval": "task: code retrieval | query: ",
+    "MultilabelClassification": "task: classification | query: ",
+    "PairClassification": "task: sentence similarity | query: ",
+    "Reranking": "task: search result | query: ",
+    "Retrieval": "task: search result | query: ",
+    "Retrieval-query": "task: search result | query: ",
+    "Retrieval-document": "title: none | text: ",
+    "STS": "task: sentence similarity | query: ",
+    "Summarization": "task: summarization | query: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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+size 1211486072

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 4,
+    "name": "4",
+    "path": "4_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 2048,
+    "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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