metadata

base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:17500
  - loss:ContrastiveLoss
widget:
  - source_sentence: 1 Scenic Unit 110
    sentences:
      - 1 Scenic Unit 110
      - 46 Drew Rear 21
      - '110 Nightin - Gale #10'
  - source_sentence: 131 Sayre Fl 1
    sentences:
      - 715 Union Unit Q
      - 1 Rustic Apt D26
      - 131 Sayre Apt 1
  - source_sentence: '731 Eaton # 1'
    sentences:
      - '1100 Wesley #1'
      - '731 Eaton #1'
      - 815 Murray Flr 2
  - source_sentence: 18 - 01 Pollitt Ste 4
    sentences:
      - 186 1st Apt 1
      - '63 Mountain # A'
      - 18 - 01 Pollitt Ste 4
  - source_sentence: '612 Madison # 2'
    sentences:
      - '421 Jersey # 1'
      - 8502 Liberty Fl 2
      - 612 Madison Apt 2
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: test
          type: test
        metrics:
          - type: pearson_cosine
            value: 0.6004811664372558
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.4540997609838606
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.4981741659289101
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.45189578750840304
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.4972646329389563
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.45172321150833644
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.6004811664029517
            name: Pearson Dot
          - type: spearman_dot
            value: 0.45184703338997106
            name: Spearman Dot
          - type: pearson_max
            value: 0.6004811664372558
            name: Pearson Max
          - type: spearman_max
            value: 0.4540997609838606
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: validation
          type: validation
        metrics:
          - type: pearson_cosine
            value: 0.9428978189133087
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6568158263615053
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.9703142955814245
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6535524581165605
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.9704178537982603
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6535890675794356
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.9428978176196957
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6535945302568601
            name: Spearman Dot
          - type: pearson_max
            value: 0.9704178537982603
            name: Pearson Max
          - type: spearman_max
            value: 0.6568158263615053
            name: Spearman Max

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

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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

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/fine-tuned-address-model-v0")
# Run inference
sentences = [
    '612 Madison # 2',
    '612 Madison Apt 2',
    '421 Jersey # 1',
]
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

Semantic Similarity

Dataset: test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6005
spearman_cosine	0.4541
pearson_manhattan	0.4982
spearman_manhattan	0.4519
pearson_euclidean	0.4973
spearman_euclidean	0.4517
pearson_dot	0.6005
spearman_dot	0.4518
pearson_max	0.6005
spearman_max	0.4541

Semantic Similarity

Dataset: validation
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9429
spearman_cosine	0.6568
pearson_manhattan	0.9703
spearman_manhattan	0.6536
pearson_euclidean	0.9704
spearman_euclidean	0.6536
pearson_dot	0.9429
spearman_dot	0.6536
pearson_max	0.9704
spearman_max	0.6568

Training Details

Training Dataset

Unnamed Dataset

Size: 17,500 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string int
details
min: 5 tokens
mean: 7.0 tokens
max: 12 tokens

min: 5 tokens
mean: 7.01 tokens
max: 12 tokens

0: ~18.70%
1: ~81.30%
Samples:

sentence_0 sentence_1 label

32 Cinder #17 32 Cinder Unit 17 1

85 Allen Apt 2R 85 Allen #2R 1

138 - 162 Martin Luther King Jr Apt 1807 138 - 162 Martin Luther King Jr Apt 1807 1

	sentence_0	sentence_1	label
type	string	string	int
details	min: 5 tokens mean: 7.0 tokens max: 12 tokens	min: 5 tokens mean: 7.01 tokens max: 12 tokens	0: ~18.70% 1: ~81.30%

sentence_0	sentence_1	label
`32 Cinder #17`	`32 Cinder Unit 17`	`1`
`85 Allen Apt 2R`	`85 Allen #2R`	`1`
`138 - 162 Martin Luther King Jr Apt 1807`	`138 - 162 Martin Luther King Jr Apt 1807`	`1`

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: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
multi_dataset_batch_sampler: round_robin

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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
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
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	test_spearman_max	validation_spearman_max
0	0	-	0.4541	-
0.0914	100	-	-	0.6494
0.1828	200	-	-	0.6567
0.2742	300	-	-	0.6566
0.3656	400	-	-	0.6568
0.4570	500	0.0056	-	0.6568
0.5484	600	-	-	0.6568
0.6399	700	-	-	0.6566
0.7313	800	-	-	0.6568
0.8227	900	-	-	0.6568
0.9141	1000	0.0026	-	0.6570
1.0	1094	-	-	0.6568
1.0055	1100	-	-	0.6568
1.0969	1200	-	-	0.6568
1.1883	1300	-	-	0.6569
1.2797	1400	-	-	0.6569
1.3711	1500	0.0021	-	0.6569
1.4625	1600	-	-	0.6570
1.5539	1700	-	-	0.6570
1.6453	1800	-	-	0.6568
1.7367	1900	-	-	0.6567
1.8282	2000	0.0018	-	0.6569
1.9196	2100	-	-	0.6571
2.0	2188	-	-	0.6571
2.0110	2200	-	-	0.6570
2.1024	2300	-	-	0.6568
2.1938	2400	-	-	0.6569
2.2852	2500	0.0016	-	0.6570
2.3766	2600	-	-	0.6569
2.4680	2700	-	-	0.6570
2.5594	2800	-	-	0.6568
2.6508	2900	-	-	0.6569
2.7422	3000	0.0014	-	0.6568
2.8336	3100	-	-	0.6569
2.9250	3200	-	-	0.6569
3.0	3282	-	-	0.6569
3.0165	3300	-	-	0.6569
3.1079	3400	-	-	0.6568
3.1993	3500	0.0014	-	0.6568
3.2907	3600	-	-	0.6569
3.3821	3700	-	-	0.6569
3.4735	3800	-	-	0.6568
3.5649	3900	-	-	0.6568
3.6563	4000	0.0013	-	0.6568
3.7477	4100	-	-	0.6568
3.8391	4200	-	-	0.6568
3.9305	4300	-	-	0.6568
4.0	4376	-	-	0.6568

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.44.2
PyTorch: 2.4.0+cu121
Accelerate: 0.33.0
Datasets: 2.21.0
Tokenizers: 0.19.1

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}
}