V2 embedding model (federal + foundation) - README.md

README.md

@@ -0,0 +1,695 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:324479
+- loss:MultipleNegativesRankingLoss
+base_model: Qwen/Qwen3-Embedding-0.6B
+widget:
+- source_sentence: 'Organization: PKF O''CONNOR DAVIES ADVISORY LLC
+
+    Location: NEW YORK, NY
+
+    Type: FOUNDATION'
+  sentences:
+  - 'Grant: Grant to OCEANA INC
+
+    Funder: PKF O''CONNOR DAVIES ADVISORY LLC (FOUNDATION)
+
+    Amount: $150,000
+
+    Description: Purpose: TO SUPPORT OCEANA''S WORK IN THE UK
+
+    Recipient Location: WASHINGTON, DC
+
+    Recipient Type: Public Charity
+
+    Amount: $150,000'
+  - 'Grant: Grant to RAINFOREST FOUNDATION INC
+
+    Funder: BPM LLP (FOUNDATION)
+
+    Amount: $100,000
+
+    Description: Purpose: RAPID RESPONSE ADDRESSING THE NEEDS OF COMMUNITIES AFFECTED
+    BY THE FIRES IN BELIZE.
+
+    Recipient Location: BROOKLYN, NY
+
+    Recipient Type: Public Charity
+
+    Amount: $100,000'
+  - 'Grant: Grant to ALONDRA ALVAREZ MURILLO
+
+    Funder: REDWITZ INC (FOUNDATION)
+
+    Amount: $300
+
+    Description: Purpose: TEACHER GRATITUDE GRANT
+
+    Recipient Location: EL CERRITO, CA
+
+    Recipient Type: EDUCATIONAL INSTITUT
+
+    Amount: $300'
+- source_sentence: 'Organization: Forvis Mazars LLP
+
+    Location: Asheville, NC
+
+    Type: FOUNDATION'
+  sentences:
+  - 'Grant: Grant to Globe Santa - The Boston Globe Foundation
+
+    Funder: Forvis Mazars LLP (FOUNDATION)
+
+    Amount: $2,000
+
+    Description: Purpose: To provide general support
+
+    Recipient Location: Boston, MA
+
+    Recipient Type: Public Charity
+
+    Amount: $2,000'
+  - 'Grant: Grant to TRIBAL ECO RESTORATION ALLIANCE
+
+    Funder: Foundation Source (FOUNDATION)
+
+    Amount: $20,000
+
+    Description: Purpose: General & Unrestricted
+
+    Recipient Location: UPPER LAKE, CA
+
+    Recipient Type: Public Charity
+
+    Amount: $20,000'
+  - 'Grant: Assessing the spatial and temporal scales of attention effects and attention-dependent
+    cholinergic release in macque V4.
+
+    Funder: National Eye Institute (FEDERAL)
+
+    Amount: $41,749
+
+    Description: Explicitly or implicitly, there are currently three competing models
+    for the role of the neuromodulator acetylcholine (ACh) in attention. The first
+    asserts that the cholinergic system is spatially imprecise and contributes to
+    a mechanism for arousal but not attention. The second states that the cholinergic
+    system is spatially imprecise and is one component of the mechanism for attention.
+    The third states that the cholinergic system is at the center of the mechanism
+    for attention (implying the sy...'
+- source_sentence: 'Organization: WITHUMSMITHBROWNPC
+
+    Location: NEW YORK, NY
+
+    Type: FOUNDATION'
+  sentences:
+  - 'Grant: Grant to XERCES SOCIETY INC
+
+    Funder: WEAVER AND TIDWELL LLP (FOUNDATION)
+
+    Amount: $200
+
+    Description: Purpose: TO FURTHER THE ORGANIZATIONS CHARITABLE OBJECTIVES
+
+    Recipient Location: NEW YORK, NY
+
+    Recipient Type: EXEMPT
+
+    Amount: $200'
+  - 'Grant: Grant to NOOGA QUEEN BEE COOPERATIVE
+
+    Funder: HEMENWAY & BARNES LLP (FOUNDATION)
+
+    Amount: $1,528
+
+    Description: Purpose: FURTHERING EDUCATION WITH RESPECT TO SCIENCE POLICY AND
+    BEEKEEPING.
+
+    Recipient Location: CHATTANOOGA, TN
+
+    Recipient Type: Non-Charity
+
+    Amount: $1,528'
+  - 'Grant: Grant to Institute for Ag & Trade Policy
+
+    Funder: WITHUMSMITHBROWNPC (FOUNDATION)
+
+    Amount: $30,000
+
+    Description: Purpose: Transform Food Systems
+
+    Recipient Location: Minneapolis, MN
+
+    Recipient Type: Public Charity
+
+    Amount: $30,000'
+- source_sentence: 'Organization: GRANT THORNTON ADVISORS LLC
+
+    Location: BOSTON, MA
+
+    Type: FOUNDATION'
+  sentences:
+  - 'Grant: Grant to SAN JUAN ROTARY FOUNDATION INC
+
+    Funder: PKF O''CONNOR DAVIES ADVISORY LLC (FOUNDATION)
+
+    Amount: $2,000
+
+    Description: Purpose: VOLUNTEER INCENTIVE PROGRAM
+
+    Recipient Location: FARMINGTON, NM
+
+    Recipient Type: Public Charity
+
+    Amount: $2,000'
+  - 'Grant: Grant to BROWN UNIVERSITY
+
+    Funder: GRANT THORNTON ADVISORS LLC (FOUNDATION)
+
+    Amount: $400
+
+    Description: Purpose: FIDELITY MATCHING GIFTS TO EDUCATION
+
+    Recipient Location: PROVIDENCE, RI
+
+    Recipient Type: Public Charity
+
+    Amount: $400'
+  - 'Grant: Experimental Study of a Model to Support Research Evidence Use for Protecting
+    Children
+
+    Funder: Eunice Kennedy Shriver National Institute of Child Health and Human Development
+    (FEDERAL)
+
+    Amount: $689,752
+
+    Description: Project Summary Protecting children through the primary prevention
+    of child abuse and neglect (CAN) is a major priority given that an estimated 1
+    in 7 children are affected each year in the U.S. and the societal cost of CAN
+    is of over $400 billion. Even though there are many evidence-based programs to
+    prevent abuse, reduce harm, and treat trauma, there remain numerous barriers for
+    policymakers to craft scientifically-informed policies to protect children. Accordingly,
+    we propose an experimental ...'
+- source_sentence: 'Organization: WITHUMSMITHBROWNPC
+
+    Location: IRVINE, CA
+
+    Type: FOUNDATION'
+  sentences:
+  - 'Grant: Grant to CENTER FOR LEADERSHIP DEVELOPMENT
+
+    Funder: BGBC ADVISORY LLC (FOUNDATION)
+
+    Amount: $1,000
+
+    Description: Purpose: TO FOSTER THE ADVANCEMENT OF MINORITY YOUTH IN CENTRAL INDIANA
+    AS FUTURE PROFESSIONAL, BUSINESS AND COMMUNITY LEADERS BY PROVIDING EXPERIENCES
+    THAT ENCOURAGE PERSONAL DEVELOPMENT AND EDUCATIONAL ATTAINMENT.
+
+    Recipient Location: INDIANAPOLIS, IN
+
+    Recipient Type: PUBLIC CHARITY
+
+    Amount: $1,000'
+  - 'Grant: Grant to Santa Barbara Botanic Garden
+
+    Funder: WITHUMSMITHBROWNPC (FOUNDATION)
+
+    Amount: $2,150
+
+    Description: Purpose: TO FURTHER THE AGENDA OF THE ORGANIZATION.
+
+    Recipient Location: Santa Barbara, CA
+
+    Recipient Type: Public Charity
+
+    Amount: $2,150'
+  - 'Grant: Grant to INTERNATIONAL RESCUE COMMITTEE INC
+
+    Funder: CLARK NUBER PS (FOUNDATION)
+
+    Amount: $200,000
+
+    Description: Purpose: ENSURING THE RIGHT TO HUMANITARIAN ASSISTANCE IN EAST AFRICA
+
+    Recipient Location: NEW YORK, NY
+
+    Recipient Type: Public Charity
+
+    Amount: $200,000'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: val similarity
+      type: val-similarity
+    metrics:
+    - type: pearson_cosine
+      value: .nan
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: .nan
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B). It maps sentences & paragraphs to a 1024-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:** [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) <!-- at revision c54f2e6e80b2d7b7de06f51cec4959f6b3e03418 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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': 512, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'})
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
+  (2): 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("sentence_transformers_model_id")
+# Run inference
+queries = [
+    "Organization: WITHUMSMITHBROWNPC\nLocation: IRVINE, CA\nType: FOUNDATION",
+]
+documents = [
+    'Grant: Grant to Santa Barbara Botanic Garden\nFunder: WITHUMSMITHBROWNPC (FOUNDATION)\nAmount: $2,150\nDescription: Purpose: TO FURTHER THE AGENDA OF THE ORGANIZATION.\nRecipient Location: Santa Barbara, CA\nRecipient Type: Public Charity\nAmount: $2,150',
+    'Grant: Grant to INTERNATIONAL RESCUE COMMITTEE INC\nFunder: CLARK NUBER PS (FOUNDATION)\nAmount: $200,000\nDescription: Purpose: ENSURING THE RIGHT TO HUMANITARIAN ASSISTANCE IN EAST AFRICA\nRecipient Location: NEW YORK, NY\nRecipient Type: Public Charity\nAmount: $200,000',
+    'Grant: Grant to CENTER FOR LEADERSHIP DEVELOPMENT\nFunder: BGBC ADVISORY LLC (FOUNDATION)\nAmount: $1,000\nDescription: Purpose: TO FOSTER THE ADVANCEMENT OF MINORITY YOUTH IN CENTRAL INDIANA AS FUTURE PROFESSIONAL, BUSINESS AND COMMUNITY LEADERS BY PROVIDING EXPERIENCES THAT ENCOURAGE PERSONAL DEVELOPMENT AND EDUCATIONAL ATTAINMENT.\nRecipient Location: INDIANAPOLIS, IN\nRecipient Type: PUBLIC CHARITY\nAmount: $1,000',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 1024] [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[0.7437, 0.0331, 0.0600]])
+```
+
+<!--
+### 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
+
+#### Semantic Similarity
+
+* Dataset: `val-similarity`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value   |
+|:--------------------|:--------|
+| pearson_cosine      | nan     |
+| **spearman_cosine** | **nan** |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+* Size: 324,479 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 16 tokens</li><li>mean: 23.39 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 83.4 tokens</li><li>max: 192 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                      | positive                                                                                                                                                                                                                                                                  |
+  |:------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Organization: DELOITTE TAX LLP<br>Location: MINNEAPOLIS, MN<br>Type: FOUNDATION</code>                | <code>Grant: Grant to WORLD HEALTH ORGANIZATION<br>Funder: DELOITTE TAX LLP (FOUNDATION)<br>Amount: $450,000<br>Description: Purpose: RESEARCH AND LEARNING OPPORTUNITIES<br>Recipient Type: GOV: EXECUTIVE ORDER<br>Amount: $450,000</code>                              |
+  | <code>Organization: Berry Dunn McNeil &amp; Parker LLC<br>Location: Portland, ME<br>Type: FOUNDATION</code> | <code>Grant: Grant to Museum of Fine Arts<br>Funder: Berry Dunn McNeil &amp; Parker LLC (FOUNDATION)<br>Amount: $3,000<br>Description: Purpose: Operations budget assistance<br>Recipient Location: Boston, MA<br>Recipient Type: Public Charity<br>Amount: $3,000</code> |
+  | <code>Organization: Aprio Advisory Group LLC<br>Location: Greenwood Village, CO<br>Type: FOUNDATION</code>  | <code>Grant: Grant to Safehouse Denver Inc<br>Funder: Aprio Advisory Group LLC (FOUNDATION)<br>Amount: $5,000<br>Description: Purpose: Survivors of domestic violence<br>Recipient Location: Denver, CO<br>Recipient Type: Public<br>Amount: $5,000</code>                |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 40,559 evaluation samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                            |
+  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                              |
+  | details | <ul><li>min: 16 tokens</li><li>mean: 23.62 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 83.31 tokens</li><li>max: 191 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                    | positive                                                                                                                                                                                                                                                                                                                                                                                            |
+  |:----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Organization: O'CONNOR MALONEY &amp; CO CPA'S<br>Location: WORCESTER, MA<br>Type: FOUNDATION</code> | <code>Grant: Grant to NIKOLAS KOJOIAN<br>Funder: O'CONNOR MALONEY &amp; CO CPA'S (FOUNDATION)<br>Amount: $3,500<br>Description: Purpose: EDUCATIONAL SCHOLARSHIP<br>Recipient Location: NORTH ATTLEBORO, MA<br>Recipient Type: I<br>Amount: $3,500</code>                                                                                                                                           |
+  | <code>Organization: WALTON ENTERPRISES LLC<br>Location: BENTONVILLE, AR<br>Type: FOUNDATION</code>        | <code>Grant: Grant to Student Achievement Partners Inc<br>Funder: WALTON ENTERPRISES LLC (FOUNDATION)<br>Amount: $429,272<br>Description: Purpose: To develop and disseminate high-quality math and literacy instructional materials to educators and publishers that accelerate student learning.<br>Recipient Location: New York, NY<br>Recipient Type: Public Charity<br>Amount: $429,272</code> |
+  | <code>Organization: FRAZIER &amp; FRAZIER ATTYS<br>Location: Jacksonville, FL<br>Type: FOUNDATION</code>  | <code>Grant: Grant to Cathedral Arts Project<br>Funder: FRAZIER &amp; FRAZIER ATTYS (FOUNDATION)<br>Amount: $2,500<br>Description: Purpose: To provide unrestricted general operating support to fulfill their mission<br>Recipient Location: Jacksonville, FL<br>Recipient Type: Public Charity<br>Amount: $2,500</code>                                                                           |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `num_train_epochs`: 1
+- `max_steps`: 1000
+- `learning_rate`: 2e-05
+- `warmup_steps`: 0.1
+- `weight_decay`: 0.01
+- `gradient_accumulation_steps`: 4
+- `fp16`: True
+- `eval_strategy`: steps
+- `per_device_eval_batch_size`: 32
+- `dataloader_num_workers`: 4
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 32
+- `num_train_epochs`: 1
+- `max_steps`: 1000
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 0.1
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 4
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: False
+- `fp16`: True
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `eval_strategy`: steps
+- `per_device_eval_batch_size`: 32
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: False
+- `hub_private_repo`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: False
+- `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
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 4
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: 0.1
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | val-similarity_spearman_cosine |
+|:------:|:----:|:-------------:|:---------------:|:------------------------------:|
+| 0.0099 | 25   | 1.7643        | -               | -                              |
+| 0.0197 | 50   | 1.0715        | -               | -                              |
+| 0.0296 | 75   | 0.4669        | -               | -                              |
+| 0.0394 | 100  | 0.3204        | 0.2283          | nan                            |
+| 0.0493 | 125  | 0.3101        | -               | -                              |
+| 0.0592 | 150  | 0.2830        | -               | -                              |
+| 0.0690 | 175  | 0.3010        | -               | -                              |
+| 0.0789 | 200  | 0.2790        | 0.2096          | nan                            |
+| 0.0888 | 225  | 0.2919        | -               | -                              |
+| 0.0986 | 250  | 0.2608        | -               | -                              |
+| 0.1085 | 275  | 0.2796        | -               | -                              |
+| 0.1183 | 300  | 0.2559        | 0.1940          | nan                            |
+| 0.1282 | 325  | 0.2376        | -               | -                              |
+| 0.1381 | 350  | 0.2491        | -               | -                              |
+| 0.1479 | 375  | 0.2307        | -               | -                              |
+| 0.1578 | 400  | 0.2233        | 0.1824          | nan                            |
+| 0.1677 | 425  | 0.2385        | -               | -                              |
+| 0.1775 | 450  | 0.2356        | -               | -                              |
+| 0.1874 | 475  | 0.2295        | -               | -                              |
+| 0.1972 | 500  | 0.2104        | 0.1721          | nan                            |
+| 0.2071 | 525  | 0.2117        | -               | -                              |
+| 0.2170 | 550  | 0.2100        | -               | -                              |
+| 0.2268 | 575  | 0.2462        | -               | -                              |
+| 0.2367 | 600  | 0.2402        | 0.1648          | nan                            |
+| 0.2465 | 625  | 0.1954        | -               | -                              |
+| 0.2564 | 650  | 0.1890        | -               | -                              |
+| 0.2663 | 675  | 0.2182        | -               | -                              |
+| 0.2761 | 700  | 0.1878        | 0.1590          | nan                            |
+| 0.2860 | 725  | 0.2252        | -               | -                              |
+| 0.2959 | 750  | 0.1886        | -               | -                              |
+| 0.3057 | 775  | 0.1879        | -               | -                              |
+| 0.3156 | 800  | 0.2009        | 0.1516          | nan                            |
+| 0.3254 | 825  | 0.1880        | -               | -                              |
+| 0.3353 | 850  | 0.1872        | -               | -                              |
+| 0.3452 | 875  | 0.1973        | -               | -                              |
+| 0.3550 | 900  | 0.1944        | 0.1474          | nan                            |
+| 0.3649 | 925  | 0.1960        | -               | -                              |
+| 0.3748 | 950  | 0.1993        | -               | -                              |
+| 0.3846 | 975  | 0.1891        | -               | -                              |
+| 0.3945 | 1000 | 0.1971        | 0.1458          | nan                            |
+
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 5.2.3
+- Transformers: 5.2.0
+- PyTorch: 2.10.0+cu128
+- Accelerate: 1.12.0
+- Datasets: 4.6.0
+- Tokenizers: 0.22.2
+
+## 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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.*
+-->
+
+---
+
+## V2.0 Update: Foundation Grants Support (February 2026)
+
+### What Changed
+
+V2 retrains the embedding model on **combined federal + foundation data**. The training set grew from federal-only pairs to **324,479 positive pairs** spanning NIH, NSF, and 37,684 private foundations.
+
+The model now understands the semantic relationship between:
+- **Federal grants**: Organization research profiles matched to NIH/NSF funding opportunities
+- **Foundation grants**: Foundation profiles matched to their actual grantmaking (recipient, purpose, amount)
+
+### Training Details (V2)
+
+- **Hardware**: NVIDIA H100 80GB HBM3
+- **Training Steps**: 1,000 (LoRA fine-tuning)
+- **Base Model**: Qwen/Qwen3-Embedding-0.6B
+- **LoRA Config**: r=16, alpha=32, target=q/k/v/o projections
+- **Effective Batch Size**: 128 (32 x 4 gradient accumulation)
+- **Final Validation Loss**: 0.1458 (steadily decreasing from 0.2283)
+
+### Downstream Impact
+
+When used as the similarity feature for the XGBoost classifier:
+
+| Metric | V1 (Federal Only) | V2 (Combined) |
+|--------|-------------------|---------------|
+| Overall AUC | 0.837 | **0.997** |
+| Federal AUC | 0.837 | **0.913** |
+
+The foundation-aware embeddings improved performance across the board, including on federal-only test data.
+
+### Version Tags
+
+- `v1.0-federal-only`: Trained on NIH + NSF data only
+- `v2.0-with-foundations`: Trained on NIH + NSF + 37K foundation grants