---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:314315
- loss:AdaptiveLayerLoss
- loss:MultipleNegativesRankingLoss
base_model: microsoft/deberta-v3-small
datasets:
- stanfordnlp/snli
- sentence-transformers/stsb
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
widget:
- source_sentence: Two teenage girls conversing next to lockers.
  sentences:
  - Girls talking about their problems next to lockers.
  - A bully tries to pop a balloon without being caught in the act.
  - Two dogs standing together in the yard.
- source_sentence: A young man in a heavy brown winter coat stands in front of a blue
    railing with his arms spread.
  sentences:
  - a boy holding onto the wall of an old brick house's raised foundation as construction
    occurs
  - The railing is in front of a frozen lake.
  - A skateboarder is doing tricks for a competition.
- source_sentence: A shirtless man with a white hat and no shoes sitting crisscross
    with his back against the wall holding up a white plastic cup.
  sentences:
  - A long-haired boy riding his skateboard at a fast pace over a stone wall with
    graffiti.
  - A man is sitting crisscross
  - a child in a black ninja suit does a kick
- source_sentence: A light colored dog leaps over a hurdle.
  sentences:
  - Men sit on the bus going to work,
  - A dog jumps over a obstacel.
  - a man standing on his motorbike.
- source_sentence: people are standing near water with a boat heading their direction
  sentences:
  - People are standing near water with a large blue boat heading their direction.
  - Two people climbing on a wooden scaffold.
  - The dogs are near the toy.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-small
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: 0.7660217567682521
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7681125489633884
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7917532885619117
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.794675885405013
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7860948725725584
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7895594746178918
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.644843928972524
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6427588138459626
      name: Spearman Dot
    - type: pearson_max
      value: 0.7917532885619117
      name: Pearson Max
    - type: spearman_max
      value: 0.794675885405013
      name: Spearman Max
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.6730608840700584
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.5814725160598755
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.7170495061078964
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.4670722782611847
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.5977392321184954
      name: Cosine Precision
    - type: cosine_recall
      value: 0.895866802979407
      name: Cosine Recall
    - type: cosine_ap
      value: 0.7193483203625508
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.6444764576541057
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 71.95508575439453
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.7094262988661364
      name: Dot F1
    - type: dot_f1_threshold
      value: 53.77289581298828
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.5779411764705882
      name: Dot Precision
    - type: dot_recall
      value: 0.9183584051409376
      name: Dot Recall
    - type: dot_ap
      value: 0.6828334101602328
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.6664644779740693
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 213.6251678466797
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.7047102517243412
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 245.20578002929688
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.5908461842625544
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.8729370527238206
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7132026586783923
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.6621426946698006
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 10.358880996704102
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.7024081560907013
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 12.010871887207031
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.5864970645792563
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.8754198919234701
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.7101786172295015
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.6730608840700584
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 213.6251678466797
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.7170495061078964
      name: Max F1
    - type: max_f1_threshold
      value: 245.20578002929688
      name: Max F1 Threshold
    - type: max_precision
      value: 0.5977392321184954
      name: Max Precision
    - type: max_recall
      value: 0.9183584051409376
      name: Max Recall
    - type: max_ap
      value: 0.7193483203625508
      name: Max Ap
---

# SentenceTransformer based on microsoft/deberta-v3-small

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) dataset. It maps sentences & paragraphs to a 768-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:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli)
- **Language:** en
<!-- - **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': False}) with Transformer model: DebertaV2Model 
  (1): Pooling({'word_embedding_dimension': 768, '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("bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerBaseline")
# Run inference
sentences = [
    'people are standing near water with a boat heading their direction',
    'People are standing near water with a large blue boat heading their direction.',
    'The dogs are near the toy.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.766      |
| **spearman_cosine** | **0.7681** |
| pearson_manhattan   | 0.7918     |
| spearman_manhattan  | 0.7947     |
| pearson_euclidean   | 0.7861     |
| spearman_euclidean  | 0.7896     |
| pearson_dot         | 0.6448     |
| spearman_dot        | 0.6428     |
| pearson_max         | 0.7918     |
| spearman_max        | 0.7947     |

#### Binary Classification

* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.6731     |
| cosine_accuracy_threshold    | 0.5815     |
| cosine_f1                    | 0.717      |
| cosine_f1_threshold          | 0.4671     |
| cosine_precision             | 0.5977     |
| cosine_recall                | 0.8959     |
| cosine_ap                    | 0.7193     |
| dot_accuracy                 | 0.6445     |
| dot_accuracy_threshold       | 71.9551    |
| dot_f1                       | 0.7094     |
| dot_f1_threshold             | 53.7729    |
| dot_precision                | 0.5779     |
| dot_recall                   | 0.9184     |
| dot_ap                       | 0.6828     |
| manhattan_accuracy           | 0.6665     |
| manhattan_accuracy_threshold | 213.6252   |
| manhattan_f1                 | 0.7047     |
| manhattan_f1_threshold       | 245.2058   |
| manhattan_precision          | 0.5908     |
| manhattan_recall             | 0.8729     |
| manhattan_ap                 | 0.7132     |
| euclidean_accuracy           | 0.6621     |
| euclidean_accuracy_threshold | 10.3589    |
| euclidean_f1                 | 0.7024     |
| euclidean_f1_threshold       | 12.0109    |
| euclidean_precision          | 0.5865     |
| euclidean_recall             | 0.8754     |
| euclidean_ap                 | 0.7102     |
| max_accuracy                 | 0.6731     |
| max_accuracy_threshold       | 213.6252   |
| max_f1                       | 0.717      |
| max_f1_threshold             | 245.2058   |
| max_precision                | 0.5977     |
| max_recall                   | 0.9184     |
| **max_ap**                   | **0.7193** |

<!--
## 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

#### stanfordnlp/snli

* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 314,315 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        | label                        |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
  | type    | string                                                                            | string                                                                           | int                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
* Samples:
  | sentence1                                                                  | sentence2                                        | label          |
  |:---------------------------------------------------------------------------|:-------------------------------------------------|:---------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>0</code> |
  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>0</code> |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>0</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "n_layers_per_step": 1,
      "last_layer_weight": 1,
      "prior_layers_weight": 1,
      "kl_div_weight": 1.2,
      "kl_temperature": 1.2
  }
  ```

### Evaluation Dataset

#### sentence-transformers/stsb

* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 14.77 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.74 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                         | sentence2                                             | score             |
  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code>  |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "n_layers_per_step": 1,
      "last_layer_weight": 1,
      "prior_layers_weight": 1,
      "kl_div_weight": 1.2,
      "kl_temperature": 1.2
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 5e-06
- `weight_decay`: 1e-07
- `num_train_epochs`: 2
- `warmup_ratio`: 0.5
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerBaselinen
- `hub_strategy`: checkpoint
- `batch_sampler`: no_duplicates

#### 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`: 32
- `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
- `learning_rate`: 5e-06
- `weight_decay`: 1e-07
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.5
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `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`: True
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerBaselinen
- `hub_strategy`: checkpoint
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step  | Training Loss | loss   | max_ap | spearman_cosine |
|:------:|:-----:|:-------------:|:------:|:------:|:---------------:|
| None   | 0     | -             | 4.1425 | -      | 0.4276          |
| 0.1001 | 983   | 4.7699        | 3.8387 | 0.6364 | -               |
| 0.2001 | 1966  | 3.5997        | 2.7649 | 0.6722 | -               |
| 0.3002 | 2949  | 2.811         | 2.3520 | 0.6838 | -               |
| 0.4003 | 3932  | 2.414         | 2.0700 | 0.6883 | -               |
| 0.5004 | 4915  | 2.186         | 1.8993 | 0.6913 | -               |
| 0.6004 | 5898  | 1.8523        | 1.5632 | 0.7045 | -               |
| 0.7005 | 6881  | 0.6415        | 1.4902 | 0.7082 | -               |
| 0.8006 | 7864  | 0.5016        | 1.4636 | 0.7108 | -               |
| 0.9006 | 8847  | 0.4194        | 1.3875 | 0.7121 | -               |
| 1.0007 | 9830  | 0.3737        | 1.3077 | 0.7117 | -               |
| 1.1008 | 10813 | 1.8087        | 1.0903 | 0.7172 | -               |
| 1.2009 | 11796 | 1.6631        | 1.0388 | 0.7180 | -               |
| 1.3009 | 12779 | 1.6161        | 1.0177 | 0.7169 | -               |
| 1.4010 | 13762 | 1.5378        | 1.0136 | 0.7148 | -               |
| 1.5011 | 14745 | 1.5215        | 1.0053 | 0.7159 | -               |
| 1.6011 | 15728 | 1.2887        | 0.9600 | 0.7166 | -               |
| 1.7012 | 16711 | 0.3058        | 0.9949 | 0.7180 | -               |
| 1.8013 | 17694 | 0.2897        | 0.9792 | 0.7186 | -               |
| 1.9014 | 18677 | 0.275         | 0.9598 | 0.7192 | -               |
| 2.0    | 19646 | -             | 0.9796 | 0.7193 | -               |
| None   | 0     | -             | 2.4594 | 0.7193 | 0.7681          |


### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.30.1
- Datasets: 2.19.2
- 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",
}
```

#### AdaptiveLayerLoss
```bibtex
@misc{li20242d,
    title={2D Matryoshka Sentence Embeddings}, 
    author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
    year={2024},
    eprint={2402.14776},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## 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.*
-->