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---
base_model: llmrails/ember-v1
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: 260 Mount Prospect Apt A4
sentences:
- 254 Mount Apt 304
- '110 Nightin - Gale #10'
- 3100 35 Apt 2
- source_sentence: '20 Harding #2'
sentences:
- '1208 Barclay #2'
- '65 Chestnut # 72'
- '20 Harding # 2'
- source_sentence: 396 Manila Apt 2B
sentences:
- 108 Gaston 1
- '175 2nd #710'
- '1 - 02 Virginia #102B'
- source_sentence: '210 Gordon Fl #1'
sentences:
- 450 Raritan Ste C
- '19 Edsall #1'
- '210 Gordon # 1'
- source_sentence: '148 1 / 2 Mill #B'
sentences:
- '7918 Pershing #'
- '91 5th #1'
- 148 1 / 2 Mill Apt B
model-index:
- name: SentenceTransformer based on llmrails/ember-v1
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: test
type: test
metrics:
- type: pearson_cosine
value: 0.7022202945624949
name: Pearson Cosine
- type: spearman_cosine
value: 0.5521115900667813
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5902198760799219
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5601831188247873
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5899864734850421
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.5598213668477258
name: Spearman Euclidean
- type: pearson_dot
value: 0.5811885721377421
name: Pearson Dot
- type: spearman_dot
value: 0.45745466821334696
name: Spearman Dot
- type: pearson_max
value: 0.7022202945624949
name: Pearson Max
- type: spearman_max
value: 0.5601831188247873
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: validation
type: validation
metrics:
- type: pearson_cosine
value: 0.9438614062035536
name: Pearson Cosine
- type: spearman_cosine
value: 0.6566095423715015
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9661648909940331
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6545897461863388
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9662831349240031
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6545799233453334
name: Spearman Euclidean
- type: pearson_dot
value: 0.7832494697144132
name: Pearson Dot
- type: spearman_dot
value: 0.6240051940767
name: Spearman Dot
- type: pearson_max
value: 0.9662831349240031
name: Pearson Max
- type: spearman_max
value: 0.6566095423715015
name: Spearman Max
---
# SentenceTransformer based on llmrails/ember-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [llmrails/ember-v1](https://huggingface.co/llmrails/ember-v1). It maps sentences & paragraphs to a 1024-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:** [llmrails/ember-v1](https://huggingface.co/llmrails/ember-v1) <!-- at revision 5e5ce5904901f6ce1c353a95020f17f09e5d021d -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **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': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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-ember-v1")
# Run inference
sentences = [
'148 1 / 2 Mill #B',
'148 1 / 2 Mill Apt B',
'7918 Pershing #',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.7022 |
| spearman_cosine | 0.5521 |
| pearson_manhattan | 0.5902 |
| spearman_manhattan | 0.5602 |
| pearson_euclidean | 0.59 |
| spearman_euclidean | 0.5598 |
| pearson_dot | 0.5812 |
| spearman_dot | 0.4575 |
| pearson_max | 0.7022 |
| **spearman_max** | **0.5602** |
#### Semantic Similarity
* Dataset: `validation`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.9439 |
| spearman_cosine | 0.6566 |
| pearson_manhattan | 0.9662 |
| spearman_manhattan | 0.6546 |
| pearson_euclidean | 0.9663 |
| spearman_euclidean | 0.6546 |
| pearson_dot | 0.7832 |
| spearman_dot | 0.624 |
| pearson_max | 0.9663 |
| **spearman_max** | **0.6566** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 17,500 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 6.97 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 6.96 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>0: ~17.80%</li><li>1: ~82.20%</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:--------------------------------|:------------------------------------|:---------------|
| <code>94 Liberty 1</code> | <code>94 Liberty Flr 1</code> | <code>1</code> |
| <code>166 Randolph Apt 1</code> | <code>166 Randolph Flr 1</code> | <code>1</code> |
| <code>400 Dutch Apt E12</code> | <code>400 Dutch Neck Apt E12</code> | <code>1</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`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `multi_dataset_batch_sampler`: round_robin
#### 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`: 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
</details>
### Training Logs
| Epoch | Step | Training Loss | test_spearman_max | validation_spearman_max |
|:------:|:----:|:-------------:|:-----------------:|:-----------------------:|
| 0 | 0 | - | 0.5602 | - |
| 0.0914 | 100 | - | - | 0.6565 |
| 0.1828 | 200 | - | - | 0.6567 |
| 0.2742 | 300 | - | - | 0.6558 |
| 0.3656 | 400 | - | - | 0.6560 |
| 0.4570 | 500 | 0.0039 | - | 0.6560 |
| 0.5484 | 600 | - | - | 0.6555 |
| 0.6399 | 700 | - | - | 0.6559 |
| 0.7313 | 800 | - | - | 0.6561 |
| 0.8227 | 900 | - | - | 0.6555 |
| 0.9141 | 1000 | 0.0019 | - | 0.6558 |
| 1.0 | 1094 | - | - | 0.6560 |
| 1.0055 | 1100 | - | - | 0.6561 |
| 1.0969 | 1200 | - | - | 0.6560 |
| 1.1883 | 1300 | - | - | 0.6559 |
| 1.2797 | 1400 | - | - | 0.6555 |
| 1.3711 | 1500 | 0.0014 | - | 0.6558 |
| 1.4625 | 1600 | - | - | 0.6560 |
| 1.5539 | 1700 | - | - | 0.6557 |
| 1.6453 | 1800 | - | - | 0.6561 |
| 1.7367 | 1900 | - | - | 0.6561 |
| 1.8282 | 2000 | 0.001 | - | 0.6562 |
| 1.9196 | 2100 | - | - | 0.6563 |
| 2.0 | 2188 | - | - | 0.6564 |
| 2.0110 | 2200 | - | - | 0.6565 |
| 2.1024 | 2300 | - | - | 0.6565 |
| 2.1938 | 2400 | - | - | 0.6560 |
| 2.2852 | 2500 | 0.0009 | - | 0.6557 |
| 2.3766 | 2600 | - | - | 0.6559 |
| 2.4680 | 2700 | - | - | 0.6560 |
| 2.5594 | 2800 | - | - | 0.6560 |
| 2.6508 | 2900 | - | - | 0.6564 |
| 2.7422 | 3000 | 0.0007 | - | 0.6565 |
| 2.8336 | 3100 | - | - | 0.6565 |
| 2.9250 | 3200 | - | - | 0.6564 |
| 3.0 | 3282 | - | - | 0.6565 |
| 3.0165 | 3300 | - | - | 0.6566 |
| 3.1079 | 3400 | - | - | 0.6568 |
| 3.1993 | 3500 | 0.0007 | - | 0.6565 |
| 3.2907 | 3600 | - | - | 0.6563 |
| 3.3821 | 3700 | - | - | 0.6564 |
| 3.4735 | 3800 | - | - | 0.6565 |
| 3.5649 | 3900 | - | - | 0.6565 |
| 3.6563 | 4000 | 0.0005 | - | 0.6566 |
| 3.7477 | 4100 | - | - | 0.6566 |
| 3.8391 | 4200 | - | - | 0.6566 |
| 3.9305 | 4300 | - | - | 0.6566 |
| 4.0 | 4376 | - | - | 0.6566 |
### 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}
}
```
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