embeddinggemma-300m-mortgage / README.md

sugiv

Fine-tuned EmbeddingGemma-300m on Mortgage Q&A Dataset

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metadata

language:
  - en
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:2699
  - loss:CachedMultipleNegativesRankingLoss
base_model: google/embeddinggemma-300m
widget:
  - source_sentence: >-
      For a conventional conforming loan, what are the common down payment
      amounts?
    sentences:
      - fannie_mae_selling_guide_chunk_001
      - fannie_mae_selling_guide
      - >-
        Standard down payment options for a conventional conforming loan range
        from 3 to 20 of the purchase price.
  - source_sentence: How is the cash required for mortgage reserves confirmed by a lender?
    sentences:
      - freddie_mac_guide
      - >-
        Lenders verify assets by requiring two months of consecutive statements
        for all checking, savings, and investment accounts.
      - freddie_mac_guide_chunk_017
  - source_sentence: >-
      What are the different types of mortgage rate locks available to
      borrowers?
    sentences:
      - freddie_mac_guide
      - >-
        Common rate locks include a 30-day lock, a 45-day lock, and a 60-day
        lock, with longer locks sometimes incurring a small fee.
      - freddie_mac_guide_chunk_014
  - source_sentence: How do lenders verify a borrowers assets for reserves?
    sentences:
      - va_chapter2_eligibility_chunk_001
      - >-
        Lenders verify assets by obtaining the two most recent monthly
        statements for all checking, savings, and investment accounts, ensuring
        the funds have been sourced and seasoned.
      - va_chapter2_eligibility
  - source_sentence: When is a borrower eligible for a streamline refinance?
    sentences:
      - >-
        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
datasets:
  - sugiv/mortgage-qa-dataset
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on google/embeddinggemma-300m
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: mortgage eval
          type: mortgage-eval
        metrics:
          - type: cosine_accuracy@1
            value: 0.34421364985163205
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.6468842729970327
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7863501483679525
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9317507418397626
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.34421364985163205
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.21562809099901084
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1572700296735905
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09317507418397623
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.34421364985163205
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.6468842729970327
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.7863501483679525
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9317507418397626
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.620931422633939
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5233208515849467
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5285370057494337
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: mortgage test
          type: mortgage-test
        metrics:
          - type: cosine_accuracy@1
            value: 0.2781065088757396
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5621301775147929
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7100591715976331
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8727810650887574
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.2781065088757396
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18737672583826429
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.14201183431952663
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08727810650887573
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.2781065088757396
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5621301775147929
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.7100591715976331
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8727810650887574
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.553056381202397
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4530313703390625
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.46216201514766103
            name: Cosine Map@100

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
Maximum Sequence Length: 2048 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- mortgage-qa-dataset
Language: en

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]])

Evaluation

Metrics

Information Retrieval

Datasets: mortgage-eval and mortgage-test
Evaluated with InformationRetrievalEvaluator

Metric	mortgage-eval	mortgage-test
cosine_accuracy@1	0.3442	0.2781
cosine_accuracy@3	0.6469	0.5621
cosine_accuracy@5	0.7864	0.7101
cosine_accuracy@10	0.9318	0.8728
cosine_precision@1	0.3442	0.2781
cosine_precision@3	0.2156	0.1874
cosine_precision@5	0.1573	0.142
cosine_precision@10	0.0932	0.0873
cosine_recall@1	0.3442	0.2781
cosine_recall@3	0.6469	0.5621
cosine_recall@5	0.7864	0.7101
cosine_recall@10	0.9318	0.8728
cosine_ndcg@10	0.6209	0.5531
cosine_mrr@10	0.5233	0.453
cosine_map@100	0.5285	0.4622

Training Details

Training Dataset

mortgage-qa-dataset

Dataset: mortgage-qa-dataset at de29792
Size: 2,699 training samples
Columns: question, answer, source_document, and source_chunk

Approximate statistics based on the first 1000 samples:

	question	answer	source_document	source_chunk
type	string	string	string	string
details	min: 9 tokens mean: 16.15 tokens max: 27 tokens	min: 14 tokens mean: 31.67 tokens max: 62 tokens	min: 8 tokens mean: 9.99 tokens max: 14 tokens	min: 14 tokens mean: 15.99 tokens max: 20 tokens

Samples:

question	answer	source_document	source_chunk
`When is a full appraisal required for a mortgage application?`	`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.`	`fha_handbook_4000_1`	`fha_handbook_4000_1_chunk_005`
`When getting a mortgage, who orders the title insurance for the lender?`	`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.`	`va_chapter4_underwriting`	`va_chapter4_underwriting_chunk_012`
`What components of a loan application does an underwriter assess?`	`Underwriters analyze the four Cs of credit: Capacity income and DTI, Capital assets and reserves, Collateral property value, and Credit credit history and score.`	`va_chapter5_processing`	`va_chapter5_processing_chunk_005`

Loss: CachedMultipleNegativesRankingLoss 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: question, answer, source_document, and source_chunk

Approximate statistics based on the first 337 samples:

	question	answer	source_document	source_chunk
type	string	string	string	string
details	min: 9 tokens mean: 16.44 tokens max: 28 tokens	min: 17 tokens mean: 32.28 tokens max: 62 tokens	min: 8 tokens mean: 10.14 tokens max: 14 tokens	min: 14 tokens mean: 16.14 tokens max: 20 tokens

Samples:

question	answer	source_document	source_chunk
`What financial metrics are crucial for an AUS to issue an approval?`	`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.`	`va_chapter4_underwriting`	`va_chapter4_underwriting_chunk_017`
`Can you explain how an LTV ratio is figured out?`	`The LTV ratio is calculated by dividing the mortgage loan amount by the appraised value or purchase price of the property, whichever is lower.`	`fannie_mae_servicing_guide`	`fannie_mae_servicing_guide_chunk_002`
`How do lenders verify a borrowers employment history?`	`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.`	`freddie_mac_guide`	`freddie_mac_guide_chunk_002`

Loss: CachedMultipleNegativesRankingLoss 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

Click to expand

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: {'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_fused
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: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	mortgage-eval_cosine_ndcg@10	mortgage-test_cosine_ndcg@10
-1	-1	-	-	0.5803	-
0.1479	25	0.1574	-	-	-
0.2959	50	0.1053	0.0722	0.5993	-
0.4438	75	0.0969	-	-	-
0.5917	100	0.0765	0.0773	0.6085	-
0.7396	125	0.079	-	-	-
0.8876	150	0.0802	0.0858	0.6056	-
1.0355	175	0.021	-	-	-
1.1834	200	0.0728	0.0549	0.6093	-
1.3314	225	0.0857	-	-	-
1.4793	250	0.071	0.0659	0.6145	-
1.6272	275	0.0633	-	-	-
1.7751	300	0.1844	0.0687	0.6209	-
1.9231	325	0.0545	-	-	-
2.0710	350	0.0474	0.0646	0.6025	-
2.2189	375	0.0702	-	-	-
2.3669	400	0.0831	0.0699	0.6026	-
2.5148	425	0.0635	-	-	-
2.6627	450	0.103	0.0674	0.6031	-
2.8107	475	0.097	-	-	-
2.9586	500	0.077	0.0686	0.6032	-
3.1065	525	0.0713	-	-	-
3.2544	550	0.1617	0.0668	0.6087	-
3.4024	575	0.1084	-	-	-
3.5503	600	0.0791	0.0658	0.6038	-
3.6982	625	0.0477	-	-	-
3.8462	650	0.0956	0.0659	0.6073	-
3.9941	675	0.0587	-	-	-
-1	-1	-	-	0.6209	0.5531

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}
}