README.md

---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4517388
- loss:ContrastiveLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: 640 prt ashley floor 10 chula vista california 91913
  sentences:
  - 10523 howard parks apartment 8 cockseysville md 21030
  - 640 prt ashley floor 10 East Gregory PW 91913
  - trailwoods radial loveland oh 4514
- source_sentence: 9036 taylorsville road louisville ky 40299-1750
  sentences:
  - '16331 northwest gearin junctn floor num 6 apt # 4 f tigard or 97223-2808'
  - 19 Brian Key walk voorhees township n. j. 08026
  - 9036 taylorsville boulevard louisville 40299-175
- source_sentence: 11 simek ln middletown township n j 07758
  sentences:
  - 248 strawberry meadows place apt 1 springdale 72764-3759
  - 11 Daniel Drive knl middletown township MT 41761
  - 1135 s westgate ave Mileshaven ca 90049
- source_sentence: so west prospect street aloha or 97078
  sentences:
  - '1300 Brittney Club plains lot # b new york cty NY 10459'
  - 527 Nicole Springs bypas rupert CA 05776
  - so wdest prospect street aloha 97078
- source_sentence: 8234 harvest bend lane laurel md 20707
  sentences:
  - 8234 harvest bend lane laurel md
  - 8702 wahl crse basement santee ca 92071
  - 310 ella street Jamesborough ne 68310
datasets:
- jarredparrett/deepparse_address_mutations_comb_3
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
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: jarredparrett/deepparse address mutations comb 3
      type: jarredparrett/deepparse_address_mutations_comb_3
    metrics:
    - type: cosine_accuracy
      value: 0.9770643339132159
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7712496519088745
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9784053285401372
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7712496519088745
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.960100255219399
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9974219699718995
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9864940067102314
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9770643339132159
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.7712496519088745
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9784053285401372
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.7712496519088745
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.960100255219399
      name: Dot Precision
    - type: dot_recall
      value: 0.9974219699718995
      name: Dot Recall
    - type: dot_ap
      value: 0.986499063941509
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9770395408321384
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 10.601512908935547
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.978383036334317
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 10.611783027648926
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.9600334406666756
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9974477502721805
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9865423177462433
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9770643339132159
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.6763879060745239
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.9784053285401372
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.6763879060745239
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.960100255219399
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9974219699718995
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9865515796011742
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9770643339132159
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 10.601512908935547
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.9784053285401372
      name: Max F1
    - type: max_f1_threshold
      value: 10.611783027648926
      name: Max F1 Threshold
    - type: max_precision
      value: 0.960100255219399
      name: Max Precision
    - type: max_recall
      value: 0.9974477502721805
      name: Max Recall
    - type: max_ap
      value: 0.9865515796011742
      name: Max Ap
    - type: cosine_accuracy
      value: 0.9770612347780813
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7710819244384766
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9783854448042815
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7710819244384766
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9600473761629129
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9974377142267394
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9865423807819248
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9770612347780813
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.7710819244384766
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9783854448042815
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.7710819244384766
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.9600473761629129
      name: Dot Precision
    - type: dot_recall
      value: 0.9974377142267394
      name: Dot Recall
    - type: dot_ap
      value: 0.9865613743522202
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9770395408321384
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 10.510114669799805
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9783637843035726
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 10.637184143066406
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.9599119169895931
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9975389354307954
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9865931109650937
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9770612347780813
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.6766358613967896
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.9783854448042815
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.6766358613967896
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.9600473761629129
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9974377142267394
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9866061739963429
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9770612347780813
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 10.510114669799805
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.9783854448042815
      name: Max F1
    - type: max_f1_threshold
      value: 10.637184143066406
      name: Max F1 Threshold
    - type: max_precision
      value: 0.9600473761629129
      name: Max Precision
    - type: max_recall
      value: 0.9975389354307954
      name: Max Recall
    - type: max_ap
      value: 0.9866061739963429
      name: Max Ap
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3) dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3)
- **Language:** en
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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): 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("jarredparrett/all-MiniLM-L6-v2_tuned_on_deepparse_address_mutations_comb_3")
# Run inference
sentences = [
    '8234 harvest bend lane laurel md 20707',
    '8234 harvest bend lane laurel md',
    '8702 wahl crse basement santee ca 92071',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Evaluation

### Metrics

#### Binary Classification
* Dataset: `jarredparrett/deepparse_address_mutations_comb_3`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.9771     |
| cosine_accuracy_threshold    | 0.7712     |
| cosine_f1                    | 0.9784     |
| cosine_f1_threshold          | 0.7712     |
| cosine_precision             | 0.9601     |
| cosine_recall                | 0.9974     |
| cosine_ap                    | 0.9865     |
| dot_accuracy                 | 0.9771     |
| dot_accuracy_threshold       | 0.7712     |
| dot_f1                       | 0.9784     |
| dot_f1_threshold             | 0.7712     |
| dot_precision                | 0.9601     |
| dot_recall                   | 0.9974     |
| dot_ap                       | 0.9865     |
| manhattan_accuracy           | 0.977      |
| manhattan_accuracy_threshold | 10.6015    |
| manhattan_f1                 | 0.9784     |
| manhattan_f1_threshold       | 10.6118    |
| manhattan_precision          | 0.96       |
| manhattan_recall             | 0.9974     |
| manhattan_ap                 | 0.9865     |
| euclidean_accuracy           | 0.9771     |
| euclidean_accuracy_threshold | 0.6764     |
| euclidean_f1                 | 0.9784     |
| euclidean_f1_threshold       | 0.6764     |
| euclidean_precision          | 0.9601     |
| euclidean_recall             | 0.9974     |
| euclidean_ap                 | 0.9866     |
| max_accuracy                 | 0.9771     |
| max_accuracy_threshold       | 10.6015    |
| max_f1                       | 0.9784     |
| max_f1_threshold             | 10.6118    |
| max_precision                | 0.9601     |
| max_recall                   | 0.9974     |
| **max_ap**                   | **0.9866** |

#### Binary Classification
* Dataset: `jarredparrett/deepparse_address_mutations_comb_3`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.9771     |
| cosine_accuracy_threshold    | 0.7711     |
| cosine_f1                    | 0.9784     |
| cosine_f1_threshold          | 0.7711     |
| cosine_precision             | 0.96       |
| cosine_recall                | 0.9974     |
| cosine_ap                    | 0.9865     |
| dot_accuracy                 | 0.9771     |
| dot_accuracy_threshold       | 0.7711     |
| dot_f1                       | 0.9784     |
| dot_f1_threshold             | 0.7711     |
| dot_precision                | 0.96       |
| dot_recall                   | 0.9974     |
| dot_ap                       | 0.9866     |
| manhattan_accuracy           | 0.977      |
| manhattan_accuracy_threshold | 10.5101    |
| manhattan_f1                 | 0.9784     |
| manhattan_f1_threshold       | 10.6372    |
| manhattan_precision          | 0.9599     |
| manhattan_recall             | 0.9975     |
| manhattan_ap                 | 0.9866     |
| euclidean_accuracy           | 0.9771     |
| euclidean_accuracy_threshold | 0.6766     |
| euclidean_f1                 | 0.9784     |
| euclidean_f1_threshold       | 0.6766     |
| euclidean_precision          | 0.96       |
| euclidean_recall             | 0.9974     |
| euclidean_ap                 | 0.9866     |
| max_accuracy                 | 0.9771     |
| max_accuracy_threshold       | 10.5101    |
| max_f1                       | 0.9784     |
| max_f1_threshold             | 10.6372    |
| max_precision                | 0.96       |
| max_recall                   | 0.9975     |
| **max_ap**                   | **0.9866** |

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### Recommendations

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## Training Details

### Training Dataset

#### deepparse_address_mutations_comb_3

* Dataset: [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3) at [7162fdc](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3/tree/7162fdce4cfcb8114dc8f64d0631dc7a48c5ab7a)
* Size: 4,517,388 training samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | label              | sentence1                                                                         | sentence2                                                                         |
  |:--------|:-------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | torch.Tensor       | string                                                                            | string                                                                            |
  | details | <ul><li></li></ul> | <ul><li>min: 8 tokens</li><li>mean: 13.21 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.54 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
  | label                                   | sentence1                                                  | sentence2                                                  |
  |:----------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|
  | <code>tensor(1, device='cuda:0')</code> | <code>12737 chesdin landng dr chesterfield va 23838</code> | <code>12737 chesdin landng dr chesterfield va</code>       |
  | <code>tensor(1, device='cuda:0')</code> | <code>6080 norh oak trafficway gladstone mo 64118</code>   | <code>6080 norh oak trafficway gladstone 64118-4896</code> |
  | <code>tensor(0, device='cuda:0')</code> | <code>242 pierce view cir wentzville mo 63385</code>       | <code>242 pierce view cir wentzville LA 63385</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": 0.5,
      "size_average": true
  }
  ```

### Evaluation Dataset

#### deepparse_address_mutations_comb_3

* Dataset: [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3) at [7162fdc](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3/tree/7162fdce4cfcb8114dc8f64d0631dc7a48c5ab7a)
* Size: 968,012 evaluation samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | label              | sentence1                                                                         | sentence2                                                                         |
  |:--------|:-------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | torch.Tensor       | string                                                                            | string                                                                            |
  | details | <ul><li></li></ul> | <ul><li>min: 8 tokens</li><li>mean: 13.24 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.45 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
  | label                                   | sentence1                                             | sentence2                                               |
  |:----------------------------------------|:------------------------------------------------------|:--------------------------------------------------------|
  | <code>tensor(1, device='cuda:0')</code> | <code>1 vincent avenue essex maryland 21221</code>    | <code>1 vincent avenue essedx MD 21221</code>           |
  | <code>tensor(1, device='cuda:0')</code> | <code>139 berg avenue hamilton tshp n.j. 08610</code> | <code>139 bcrg avenue hamilton tshp n.j. 08610</code>   |
  | <code>tensor(1, device='cuda:0')</code> | <code>714 havard rd houston texas 77336</code>        | <code>714 havaplns plns houston texas 77336-3120</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": 0.5,
      "size_average": true
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 1024
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
- `fp16`: 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`: 1024
- `per_device_eval_batch_size`: 1024
- `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`: 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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step  | Training Loss | loss   | jarredparrett/deepparse_address_mutations_comb_3_max_ap |
|:------:|:-----:|:-------------:|:------:|:-------------------------------------------------------:|
| 0.1133 | 500   | 0.0191        | 0.0131 | 0.8459                                                  |
| 0.2267 | 1000  | 0.0112        | 0.0091 | 0.8887                                                  |
| 0.3400 | 1500  | 0.0086        | 0.0067 | 0.9346                                                  |
| 0.4533 | 2000  | 0.0064        | 0.0044 | 0.9604                                                  |
| 0.5666 | 2500  | 0.0049        | 0.0037 | 0.9722                                                  |
| 0.6800 | 3000  | 0.0042        | 0.0033 | 0.9761                                                  |
| 0.7933 | 3500  | 0.0039        | 0.0032 | 0.9808                                                  |
| 0.9066 | 4000  | 0.0037        | 0.0029 | 0.9825                                                  |
| 1.0197 | 4500  | 0.0035        | 0.0028 | 0.9826                                                  |
| 1.1330 | 5000  | 0.0033        | 0.0028 | 0.9836                                                  |
| 1.2464 | 5500  | 0.0032        | 0.0027 | 0.9845                                                  |
| 1.3597 | 6000  | 0.0031        | 0.0026 | 0.9853                                                  |
| 1.4730 | 6500  | 0.003         | 0.0025 | 0.9857                                                  |
| 1.5864 | 7000  | 0.003         | 0.0025 | 0.9859                                                  |
| 1.6997 | 7500  | 0.0029        | 0.0025 | 0.9862                                                  |
| 1.8130 | 8000  | 0.0028        | 0.0024 | 0.9864                                                  |
| 1.9263 | 8500  | 0.0028        | 0.0024 | 0.9861                                                  |
| 2.0394 | 9000  | 0.0028        | 0.0024 | 0.9864                                                  |
| 2.1528 | 9500  | 0.0027        | 0.0024 | 0.9864                                                  |
| 2.2661 | 10000 | 0.0027        | 0.0024 | 0.9865                                                  |
| 2.3794 | 10500 | 0.0027        | 0.0023 | 0.9866                                                  |
| 2.4927 | 11000 | 0.0026        | 0.0023 | 0.9866                                                  |
| 2.6061 | 11500 | 0.0026        | 0.0023 | 0.9865                                                  |
| 2.7194 | 12000 | 0.0026        | 0.0023 | 0.9865                                                  |
| 2.8327 | 12500 | 0.0026        | 0.0023 | 0.9865                                                  |
| 2.9461 | 13000 | 0.0026        | 0.0023 | 0.9866                                                  |
| 2.9995 | 13236 | -             | -      | 0.9866                                                  |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu121
- Accelerate: 1.1.1
- Datasets: 3.2.0
- Tokenizers: 0.20.3

## 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}
}
```

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