embeddinggemma-300m-haystack-contrastive-thin-fixed

EmbeddingGemma-300m trained to measure coverage

This is a sentence-transformers model finetuned from 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 <!-- 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
• 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("mancer146/embeddinggemma-300m-haystack-contrastive-thin-fixed")
# 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\ncounty_name: Collier\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([[0.9881, 0.8106, 0.6785]])

<!-- ### 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>

<!-- ## 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: 165.35 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> with these parameters:

  {
      "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

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

@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

@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}
}

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