--- 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](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/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](https://huggingface.co/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](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/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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 | | | | * 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](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`: 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 ```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} } ```