metadata

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 model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the 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
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- deepparse_address_mutations_comb_3
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': 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:

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("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]

Evaluation

Metrics

Binary Classification

Dataset: jarredparrett/deepparse_address_mutations_comb_3
Evaluated with 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 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

Training Details

Training Dataset

deepparse_address_mutations_comb_3

Dataset: deepparse_address_mutations_comb_3 at 7162fdc
Size: 4,517,388 training samples
Columns: label, sentence1, and sentence2
Approximate statistics based on the first 1000 samples:
label sentence1 sentence2
type torch.Tensor string string
details

min: 8 tokens
mean: 13.21 tokens
max: 22 tokens

min: 6 tokens
mean: 13.54 tokens
max: 22 tokens

	label	sentence1	sentence2
type	torch.Tensor	string	string
details		min: 8 tokens mean: 13.21 tokens max: 22 tokens	min: 6 tokens mean: 13.54 tokens max: 22 tokens

Samples:

label	sentence1	sentence2
`tensor(1, device='cuda:0')`	`12737 chesdin landng dr chesterfield va 23838`	`12737 chesdin landng dr chesterfield va`
`tensor(1, device='cuda:0')`	`6080 norh oak trafficway gladstone mo 64118`	`6080 norh oak trafficway gladstone 64118-4896`
`tensor(0, device='cuda:0')`	`242 pierce view cir wentzville mo 63385`	`242 pierce view cir wentzville LA 63385`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Evaluation Dataset

deepparse_address_mutations_comb_3

Dataset: deepparse_address_mutations_comb_3 at 7162fdc
Size: 968,012 evaluation samples
Columns: label, sentence1, and sentence2
Approximate statistics based on the first 1000 samples:
label sentence1 sentence2
type torch.Tensor string string
details

min: 8 tokens
mean: 13.24 tokens
max: 22 tokens

min: 7 tokens
mean: 13.45 tokens
max: 27 tokens

	label	sentence1	sentence2
type	torch.Tensor	string	string
details		min: 8 tokens mean: 13.24 tokens max: 22 tokens	min: 7 tokens mean: 13.45 tokens max: 27 tokens

Samples:

label	sentence1	sentence2
`tensor(1, device='cuda:0')`	`1 vincent avenue essex maryland 21221`	`1 vincent avenue essedx MD 21221`
`tensor(1, device='cuda:0')`	`139 berg avenue hamilton tshp n.j. 08610`	`139 bcrg avenue hamilton tshp n.j. 08610`
`tensor(1, device='cuda:0')`	`714 havard rd houston texas 77336`	`714 havaplns plns houston texas 77336-3120`

Loss: ContrastiveLoss with these parameters:

{
    "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

Click to expand

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

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

@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}
}