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Add new SentenceTransformer model.
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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

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

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

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
  • 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}
}