sentence-transformers onnx safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:1290285 loss:TemperatureScaledCosineSimilarityLoss arxiv:1908.10084 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1290285
- loss:TemperatureScaledCosineSimilarityLoss
widget:
- source_sentence: garden area
  sentences:
  - minimalistisk design
  - grönområde i närheten
  - vaaleat puusävyt
- source_sentence: ainutlaatuinen rakennus
  sentences:
  - sovrum med parkettgolv
  - light wood flooring
  - modern fixtures
- source_sentence: förortsområde
  sentences:
  - lush greenery
  - well
  - hyvin valaistu
- source_sentence: single-level
  sentences:
  - lähellä luontoa
  - trappor till entrén
  - light colored sofa
- source_sentence: gravel parking
  sentences:
  - asfalterad uppfart
  - rauhallinen naapurusto
  - asfalterad uppfart
model-index:
- name: SentenceTransformer
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: trait eval
      type: trait-eval
    metrics:
    - type: pearson_cosine
      value: 0.7845309961698034
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8182212433993569
      name: Spearman Cosine
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **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': 128, '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})
)
```

## 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'gravel parking',
    'asfalterad uppfart',
    'rauhallinen naapurusto',
]
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Semantic Similarity

* Dataset: `trait-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7845     |
| **spearman_cosine** | **0.8182** |

<!--
## 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.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 1,290,285 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                                                                           | float                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 5.36 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.47 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.34</li><li>max: 0.9</li></ul> |
* Samples:
  | sentence_0                          | sentence_1                  | label            |
  |:------------------------------------|:----------------------------|:-----------------|
  | <code>kerrostalo</code>             | <code>puuaita</code>        | <code>0.1</code> |
  | <code>corner unit</code>            | <code>large yard</code>     | <code>0.3</code> |
  | <code>easy access to highway</code> | <code>oma pysäköinti</code> | <code>0.3</code> |
* Loss: <code>__main__.TemperatureScaledCosineSimilarityLoss</code> with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 5
- `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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 5
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step   | Training Loss | trait-eval_spearman_cosine |
|:------:|:------:|:-------------:|:--------------------------:|
| 0.0248 | 500    | 0.1261        | -                          |
| 0.0496 | 1000   | 0.1155        | 0.2026                     |
| 0.0744 | 1500   | 0.119         | -                          |
| 0.0992 | 2000   | 0.1193        | 0.2516                     |
| 0.1240 | 2500   | 0.118         | -                          |
| 0.1488 | 3000   | 0.1151        | 0.2638                     |
| 0.1736 | 3500   | 0.1152        | -                          |
| 0.1984 | 4000   | 0.1093        | 0.2700                     |
| 0.2232 | 4500   | 0.1077        | -                          |
| 0.2480 | 5000   | 0.109         | 0.2942                     |
| 0.2728 | 5500   | 0.1015        | -                          |
| 0.2976 | 6000   | 0.1059        | 0.3171                     |
| 0.3224 | 6500   | 0.1052        | -                          |
| 0.3472 | 7000   | 0.1042        | 0.3114                     |
| 0.3720 | 7500   | 0.1006        | -                          |
| 0.3968 | 8000   | 0.1007        | 0.3328                     |
| 0.4216 | 8500   | 0.1013        | -                          |
| 0.4464 | 9000   | 0.0938        | 0.3407                     |
| 0.4712 | 9500   | 0.0971        | -                          |
| 0.4960 | 10000  | 0.0976        | 0.3430                     |
| 0.5208 | 10500  | 0.0945        | -                          |
| 0.5456 | 11000  | 0.0937        | 0.3603                     |
| 0.5704 | 11500  | 0.0919        | -                          |
| 0.5952 | 12000  | 0.0893        | 0.3916                     |
| 0.6200 | 12500  | 0.0904        | -                          |
| 0.6448 | 13000  | 0.0866        | 0.3931                     |
| 0.6696 | 13500  | 0.087         | -                          |
| 0.6944 | 14000  | 0.0848        | 0.4178                     |
| 0.7192 | 14500  | 0.087         | -                          |
| 0.7440 | 15000  | 0.0827        | 0.4218                     |
| 0.7688 | 15500  | 0.0851        | -                          |
| 0.7936 | 16000  | 0.0807        | 0.4414                     |
| 0.8184 | 16500  | 0.0803        | -                          |
| 0.8432 | 17000  | 0.0803        | 0.4526                     |
| 0.8680 | 17500  | 0.0784        | -                          |
| 0.8928 | 18000  | 0.0776        | 0.4592                     |
| 0.9176 | 18500  | 0.0761        | -                          |
| 0.9424 | 19000  | 0.0739        | 0.4856                     |
| 0.9672 | 19500  | 0.0724        | -                          |
| 0.9920 | 20000  | 0.0738        | 0.4928                     |
| 1.0    | 20161  | -             | 0.4863                     |
| 1.0168 | 20500  | 0.0707        | -                          |
| 1.0416 | 21000  | 0.0699        | 0.4950                     |
| 1.0664 | 21500  | 0.0637        | -                          |
| 1.0912 | 22000  | 0.0677        | 0.5000                     |
| 1.1160 | 22500  | 0.0638        | -                          |
| 1.1408 | 23000  | 0.0653        | 0.5306                     |
| 1.1656 | 23500  | 0.0661        | -                          |
| 1.1904 | 24000  | 0.0679        | 0.5292                     |
| 1.2152 | 24500  | 0.0618        | -                          |
| 1.2400 | 25000  | 0.0616        | 0.5253                     |
| 1.2648 | 25500  | 0.0626        | -                          |
| 1.2896 | 26000  | 0.0624        | 0.5422                     |
| 1.3144 | 26500  | 0.0613        | -                          |
| 1.3392 | 27000  | 0.0623        | 0.5515                     |
| 1.3640 | 27500  | 0.0601        | -                          |
| 1.3888 | 28000  | 0.0589        | 0.5606                     |
| 1.4136 | 28500  | 0.06          | -                          |
| 1.4384 | 29000  | 0.0598        | 0.5774                     |
| 1.4632 | 29500  | 0.0553        | -                          |
| 1.4880 | 30000  | 0.0596        | 0.5812                     |
| 1.5128 | 30500  | 0.0547        | -                          |
| 1.5376 | 31000  | 0.0542        | 0.5956                     |
| 1.5624 | 31500  | 0.0547        | -                          |
| 1.5872 | 32000  | 0.0545        | 0.6053                     |
| 1.6120 | 32500  | 0.0525        | -                          |
| 1.6368 | 33000  | 0.0545        | 0.6137                     |
| 1.6616 | 33500  | 0.0532        | -                          |
| 1.6864 | 34000  | 0.0525        | 0.6213                     |
| 1.7112 | 34500  | 0.0529        | -                          |
| 1.7360 | 35000  | 0.0515        | 0.6152                     |
| 1.7608 | 35500  | 0.0506        | -                          |
| 1.7856 | 36000  | 0.0502        | 0.6211                     |
| 1.8104 | 36500  | 0.0513        | -                          |
| 1.8352 | 37000  | 0.0476        | 0.6344                     |
| 1.8600 | 37500  | 0.0491        | -                          |
| 1.8848 | 38000  | 0.0485        | 0.6438                     |
| 1.9096 | 38500  | 0.0488        | -                          |
| 1.9344 | 39000  | 0.0471        | 0.6591                     |
| 1.9592 | 39500  | 0.049         | -                          |
| 1.9840 | 40000  | 0.0486        | 0.6692                     |
| 2.0    | 40322  | -             | 0.6619                     |
| 2.0088 | 40500  | 0.044         | -                          |
| 2.0336 | 41000  | 0.0407        | 0.6729                     |
| 2.0584 | 41500  | 0.0422        | -                          |
| 2.0832 | 42000  | 0.0427        | 0.6775                     |
| 2.1080 | 42500  | 0.0425        | -                          |
| 2.1328 | 43000  | 0.043         | 0.6772                     |
| 2.1576 | 43500  | 0.0426        | -                          |
| 2.1824 | 44000  | 0.04          | 0.6877                     |
| 2.2072 | 44500  | 0.041         | -                          |
| 2.2320 | 45000  | 0.0422        | 0.6885                     |
| 2.2568 | 45500  | 0.04          | -                          |
| 2.2816 | 46000  | 0.0399        | 0.6903                     |
| 2.3064 | 46500  | 0.0414        | -                          |
| 2.3312 | 47000  | 0.0397        | 0.7008                     |
| 2.3560 | 47500  | 0.0406        | -                          |
| 2.3808 | 48000  | 0.0395        | 0.7004                     |
| 2.4056 | 48500  | 0.0375        | -                          |
| 2.4304 | 49000  | 0.0391        | 0.6964                     |
| 2.4552 | 49500  | 0.0378        | -                          |
| 2.4800 | 50000  | 0.0379        | 0.7092                     |
| 2.5048 | 50500  | 0.0394        | -                          |
| 2.5296 | 51000  | 0.0399        | 0.7122                     |
| 2.5544 | 51500  | 0.0357        | -                          |
| 2.5792 | 52000  | 0.0362        | 0.7170                     |
| 2.6040 | 52500  | 0.0383        | -                          |
| 2.6288 | 53000  | 0.0396        | 0.7218                     |
| 2.6536 | 53500  | 0.037         | -                          |
| 2.6784 | 54000  | 0.0372        | 0.7312                     |
| 2.7032 | 54500  | 0.0372        | -                          |
| 2.7280 | 55000  | 0.0365        | 0.7303                     |
| 2.7528 | 55500  | 0.0364        | -                          |
| 2.7776 | 56000  | 0.0356        | 0.7366                     |
| 2.8024 | 56500  | 0.034         | -                          |
| 2.8272 | 57000  | 0.036         | 0.7415                     |
| 2.8520 | 57500  | 0.035         | -                          |
| 2.8768 | 58000  | 0.0355        | 0.7402                     |
| 2.9016 | 58500  | 0.0331        | -                          |
| 2.9264 | 59000  | 0.0345        | 0.7475                     |
| 2.9512 | 59500  | 0.0345        | -                          |
| 2.9760 | 60000  | 0.0348        | 0.7489                     |
| 3.0    | 60483  | -             | 0.7500                     |
| 3.0008 | 60500  | 0.0351        | -                          |
| 3.0256 | 61000  | 0.0294        | 0.7537                     |
| 3.0504 | 61500  | 0.0297        | -                          |
| 3.0752 | 62000  | 0.0301        | 0.7557                     |
| 3.1000 | 62500  | 0.0311        | -                          |
| 3.1248 | 63000  | 0.0314        | 0.7628                     |
| 3.1496 | 63500  | 0.0288        | -                          |
| 3.1744 | 64000  | 0.0311        | 0.7713                     |
| 3.1992 | 64500  | 0.03          | -                          |
| 3.2240 | 65000  | 0.0307        | 0.7728                     |
| 3.2488 | 65500  | 0.0321        | -                          |
| 3.2736 | 66000  | 0.028         | 0.7726                     |
| 3.2984 | 66500  | 0.031         | -                          |
| 3.3232 | 67000  | 0.0283        | 0.7717                     |
| 3.3480 | 67500  | 0.0302        | -                          |
| 3.3728 | 68000  | 0.0302        | 0.7808                     |
| 3.3976 | 68500  | 0.0303        | -                          |
| 3.4224 | 69000  | 0.0285        | 0.7790                     |
| 3.4472 | 69500  | 0.0304        | -                          |
| 3.4720 | 70000  | 0.0287        | 0.7856                     |
| 3.4969 | 70500  | 0.0301        | -                          |
| 3.5217 | 71000  | 0.0285        | 0.7886                     |
| 3.5465 | 71500  | 0.0295        | -                          |
| 3.5713 | 72000  | 0.0296        | 0.7899                     |
| 3.5961 | 72500  | 0.0269        | -                          |
| 3.6209 | 73000  | 0.0278        | 0.7911                     |
| 3.6457 | 73500  | 0.0299        | -                          |
| 3.6705 | 74000  | 0.0285        | 0.7898                     |
| 3.6953 | 74500  | 0.0286        | -                          |
| 3.7201 | 75000  | 0.0281        | 0.7891                     |
| 3.7449 | 75500  | 0.0308        | -                          |
| 3.7697 | 76000  | 0.0288        | 0.7893                     |
| 3.7945 | 76500  | 0.0283        | -                          |
| 3.8193 | 77000  | 0.0264        | 0.7953                     |
| 3.8441 | 77500  | 0.0265        | -                          |
| 3.8689 | 78000  | 0.0271        | 0.7942                     |
| 3.8937 | 78500  | 0.0263        | -                          |
| 3.9185 | 79000  | 0.0278        | 0.7932                     |
| 3.9433 | 79500  | 0.0258        | -                          |
| 3.9681 | 80000  | 0.028         | 0.7996                     |
| 3.9929 | 80500  | 0.0284        | -                          |
| 4.0    | 80644  | -             | 0.8017                     |
| 4.0177 | 81000  | 0.0258        | 0.8022                     |
| 4.0425 | 81500  | 0.027         | -                          |
| 4.0673 | 82000  | 0.0228        | 0.8034                     |
| 4.0921 | 82500  | 0.0259        | -                          |
| 4.1169 | 83000  | 0.0257        | 0.8057                     |
| 4.1417 | 83500  | 0.0248        | -                          |
| 4.1665 | 84000  | 0.025         | 0.8060                     |
| 4.1913 | 84500  | 0.024         | -                          |
| 4.2161 | 85000  | 0.0267        | 0.8084                     |
| 4.2409 | 85500  | 0.0244        | -                          |
| 4.2657 | 86000  | 0.0261        | 0.8058                     |
| 4.2905 | 86500  | 0.0256        | -                          |
| 4.3153 | 87000  | 0.0252        | 0.8061                     |
| 4.3401 | 87500  | 0.0246        | -                          |
| 4.3649 | 88000  | 0.0243        | 0.8095                     |
| 4.3897 | 88500  | 0.0243        | -                          |
| 4.4145 | 89000  | 0.0251        | 0.8113                     |
| 4.4393 | 89500  | 0.0247        | -                          |
| 4.4641 | 90000  | 0.0239        | 0.8109                     |
| 4.4889 | 90500  | 0.0248        | -                          |
| 4.5137 | 91000  | 0.0235        | 0.8129                     |
| 4.5385 | 91500  | 0.0246        | -                          |
| 4.5633 | 92000  | 0.0231        | 0.8132                     |
| 4.5881 | 92500  | 0.0254        | -                          |
| 4.6129 | 93000  | 0.0249        | 0.8140                     |
| 4.6377 | 93500  | 0.0229        | -                          |
| 4.6625 | 94000  | 0.025         | 0.8143                     |
| 4.6873 | 94500  | 0.0244        | -                          |
| 4.7121 | 95000  | 0.0227        | 0.8158                     |
| 4.7369 | 95500  | 0.0223        | -                          |
| 4.7617 | 96000  | 0.0232        | 0.8166                     |
| 4.7865 | 96500  | 0.024         | -                          |
| 4.8113 | 97000  | 0.0243        | 0.8170                     |
| 4.8361 | 97500  | 0.0229        | -                          |
| 4.8609 | 98000  | 0.0243        | 0.8172                     |
| 4.8857 | 98500  | 0.0223        | -                          |
| 4.9105 | 99000  | 0.0252        | 0.8176                     |
| 4.9353 | 99500  | 0.0242        | -                          |
| 4.9601 | 100000 | 0.0221        | 0.8182                     |
| 4.9849 | 100500 | 0.022         | -                          |
| 5.0    | 100805 | -             | 0.8182                     |

</details>

### Framework Versions
- Python: 3.13.2
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.7.0.dev20250221+cu128
- Accelerate: 1.4.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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",
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->

