sugiv/embeddinggemma-300m-mortgage

SentenceTransformer based on google/embeddinggemma-300m

This is a sentence-transformers model finetuned from google/embeddinggemma-300m on the mortgage-qa-dataset 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 <!-- at revision 64614b0b8b64f0c6c1e52b07e4e9a4e8fe4d2da2 -->
• —Maximum Sequence Length: 2048 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity
• —Training Dataset:
• —mortgage-qa-dataset
• —Language: en <!-- - License: Unknown -->

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sugiv/embeddinggemma-300m-mortgage")
# Run inference
queries = [
    "When is a borrower eligible for a streamline refinance?",
]
documents = [
    'A borrower is eligible for a streamline refinance if they have made at least six consecutive on-time payments on their current mortgage.',
    'fha_handbook_4000_1',
    'fha_handbook_4000_1_chunk_007',
]
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([[ 0.8276, -0.0791, -0.0792]])

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Evaluation

Metrics

Information Retrieval

• —Datasets: mortgage-eval and mortgage-test
• —Evaluated with <code>InformationRetrievalEvaluator</code>

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Training Details

Training Dataset

mortgage-qa-dataset

• —Dataset: mortgage-qa-dataset at de29792
• —Size: 2,699 training samples
• —Columns: <code>question</code>, <code>answer</code>, <code>sourcedocument</code>, and <code>sourcechunk</code>
• —Approximate statistics based on the first 1000 samples: | | question | answer | sourcedocument | sourcechunk | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 16.15 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 31.67 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 9.99 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 15.99 tokens</li><li>max: 20 tokens</li></ul> |
• —Samples: | question | answer | sourcedocument | sourcechunk | |:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------|:------------------------------------------------| | <code>When is a full appraisal required for a mortgage application?</code> | <code>A full appraisal is required for most transactions, but it can be waived for certain streamlined refinance programs if the Loan-to-Value LTV ratio is 90 or less.</code> | <code>fhahandbook40001</code> | <code>fhahandbook40001chunk005</code> | | <code>When getting a mortgage, who orders the title insurance for the lender?</code> | <code>While often coordinated by the settlement agent, the lender typically requires and is the ultimate recipient of the lenders title insurance policy to protect their financial interest.</code> | <code>vachapter4underwriting</code> | <code>vachapter4underwritingchunk012</code> | | <code>What components of a loan application does an underwriter assess?</code> | <code>Underwriters analyze the four Cs of credit: Capacity income and DTI, Capital assets and reserves, Collateral property value, and Credit credit history and score.</code> | <code>vachapter5processing</code> | <code>vachapter5processingchunk005</code> |
• —Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 32,
      "gather_across_devices": false
  }

Evaluation Dataset

mortgage-qa-dataset

• —Dataset: mortgage-qa-dataset at de29792
• —Size: 337 evaluation samples
• —Columns: <code>question</code>, <code>answer</code>, <code>sourcedocument</code>, and <code>sourcechunk</code>
• —Approximate statistics based on the first 337 samples: | | question | answer | sourcedocument | sourcechunk | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 16.44 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 32.28 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 10.14 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 16.14 tokens</li><li>max: 20 tokens</li></ul> |
• —Samples: | question | answer | sourcedocument | sourcechunk | |:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------|:--------------------------------------------------| | <code>What financial metrics are crucial for an AUS to issue an approval?</code> | <code>Key AUS factors include credit score, loan-to-value ratio, debt-to-income ratio, and the overall strength and stability of the borrowers financial profile.</code> | <code>vachapter4underwriting</code> | <code>vachapter4underwritingchunk017</code> | | <code>Can you explain how an LTV ratio is figured out?</code> | <code>The LTV ratio is calculated by dividing the mortgage loan amount by the appraised value or purchase price of the property, whichever is lower.</code> | <code>fanniemaeservicingguide</code> | <code>fanniemaeservicingguidechunk002</code> | | <code>How do lenders verify a borrowers employment history?</code> | <code>Lenders verify employment by contacting employers directly and typically require a two-year history, which may be confirmed via recent pay stubs and W-2 forms.</code> | <code>freddiemacguide</code> | <code>freddiemacguidechunk002</code> |
• —Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 32,
      "gather_across_devices": false
  }

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 16
• —per_device_eval_batch_size: 16
• —learning_rate: 3e-06
• —num_train_epochs: 4
• —warmup_steps: 100
• —fp16: True
• —load_best_model_at_end: True
• —batch_sampler: no_duplicates

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: 16
• —per_device_eval_batch_size: 16
• —per_gpu_train_batch_size: None
• —per_gpu_eval_batch_size: None
• —gradient_accumulation_steps: 1
• —eval_accumulation_steps: None
• —torch_empty_cache_steps: None
• —learning_rate: 3e-06
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —max_grad_norm: 1.0
• —num_train_epochs: 4
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: {}
• —warmup_ratio: 0.0
• —warmup_steps: 100
• —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: True
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_min_num_params: 0
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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: None
• —batch_sampler: no_duplicates
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

• —The bold row denotes the saved checkpoint.

Framework Versions

• —Python: 3.11.11
• —Sentence Transformers: 5.1.0
• —Transformers: 4.57.0.dev0
• —PyTorch: 2.8.0.dev20250319+cu128
• —Accelerate: 1.10.1
• —Datasets: 4.0.0
• —Tokenizers: 0.22.0

Citation

BibTeX

Sentence Transformers

@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",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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