sentence-transformers safetensors sentence-similarity feature-extraction generated_from_trainer dataset_size:35934 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss dataset:Corran/SciGenTriplets arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- Corran/SciGenTriplets
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:35934
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Stating purpose of the current research with reference to gaps
    or issues in the literature
  sentences:
  - During the 15-year study, 10% of the osseointegrated implants in the edentulous
    jaw showed signs of peri-implantitis, leading to their failure.
  - This paper provides an in-depth exploration of the qualitative case study methodology,
    addressing the lack of comprehensive guidance for novice researchers in this area.
  - As a novice researcher in management science, I have been drawn to the qualitative
    case study methodology due to its ability to provide rich, in-depth insights into
    complex real-world situations.
- source_sentence: Indicating missing, weak, or contradictory evidence
  sentences:
  - This paper contributes to the literature on the financial system by examining
    the relationship between bank size, bank capital, and the bank lending channel
    using a unique dataset of US banks during the global financial crisis.
  - A total of 150 patients with a clinical diagnosis of osteoarthritis of the hip
    or knee, according to the American College of Rheumatology criteria, were included
    in the study.
  - Despite the widespread use of the WOMAC (Western Ontario and McMaster Universities
    Osteoarthritis Index) questionnaire in clinical practice and research, there is
    a lack of consensus regarding its responsiveness to antirheumatic drug therapy
    in patients with osteoarthritis of the hip or knee.
- source_sentence: 'Establishing the importance of the topic for the world or society:
    time frame given'
  sentences:
  - The Th/Hf ratios of the basaltic lavas from the British Tertiary Volcanic Province
    range from 4.2 to 5.5, as shown in Table 1.
  - The use of organometal halide perovskites as visible-light sensitizers for photovoltaic
    cells has gained significant attention in the optoelectronics community due to
    their promising photovoltaic performance and cost-effective fabrication since
    the late 2000s.
  - Table 1 summarizes the power conversion efficiencies (PCEs) and certifications
    of the best-performing perovskite solar cells reported in the literature.
- source_sentence: Describing the research design and the methods used
  sentences:
  - This study aims to evaluate the efficacy and safety of preoperative radiotherapy
    followed by total mesorectal excision in the treatment of resectable rectal cancer.
  - TREE-PUZZLE's parallel computing implementation significantly reduces the time
    required for maximum likelihood phylogenetic analysis compared to traditional
    methods, supporting previous findings of the importance of parallelization in
    phylogenetics.
  - This study investigates the efficacy of preoperative radiotherapy followed by
    total mesorectal excision in the treatment of resectable rectal cancer.
- source_sentence: 'Surveys and interviews: Introducing excerpts from interview data'
  sentences:
  - Previous research on international trade under the WTO regime has explored various
    approaches to understanding the uneven promotion of trade (Hoekstra & Kostecki,
    2001; Cline, 2004, ...).
  - Through surveys and interviews, multiliterate teachers expressed a shared belief
    in the importance of fostering students' ability to navigate multiple discourse
    communities.
  - The authors employ a constructivist approach to learning, where students build
    knowledge through active engagement with multimedia texts and collaborative discussions.
model-index:
- name: SentenceTransformer
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: SciGen Eval Set
      type: SciGen-Eval-Set
    metrics:
    - type: cosine_accuracy@1
      value: 0.8918076580587712
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9307658058771149
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9481300089047195
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9668299198575245
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8918076580587712
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3102552686257049
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18962600178094388
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09668299198575243
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8918076580587712
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9307658058771149
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9481300089047195
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9668299198575245
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9279217256301748
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9156546382281018
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9171082586239344
      name: Cosine Map@100
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained on the [sci_gen_colbert_triplets](https://huggingface.co/datasets/Corran/SciGenColbertTriplets) 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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** inf tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [sci_gen_colbert_triplets](https://huggingface.co/datasets/Corran/SciGenColbertTriplets)
<!-- - **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): StaticEmbedding(
    (embedding): EmbeddingBag(30522, 768, mode='mean')
  )
)
```

## 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("Corran/SciGenNomicEmbedStatic")
# Run inference
sentences = [
    'Surveys and interviews: Introducing excerpts from interview data',
    "Through surveys and interviews, multiliterate teachers expressed a shared belief in the importance of fostering students' ability to navigate multiple discourse communities.",
    'The authors employ a constructivist approach to learning, where students build knowledge through active engagement with multimedia texts and collaborative discussions.',
]
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

#### Information Retrieval

* Dataset: `SciGen-Eval-Set`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8918     |
| cosine_accuracy@3   | 0.9308     |
| cosine_accuracy@5   | 0.9481     |
| cosine_accuracy@10  | 0.9668     |
| cosine_precision@1  | 0.8918     |
| cosine_precision@3  | 0.3103     |
| cosine_precision@5  | 0.1896     |
| cosine_precision@10 | 0.0967     |
| cosine_recall@1     | 0.8918     |
| cosine_recall@3     | 0.9308     |
| cosine_recall@5     | 0.9481     |
| cosine_recall@10    | 0.9668     |
| **cosine_ndcg@10**  | **0.9279** |
| cosine_mrr@10       | 0.9157     |
| cosine_map@100      | 0.9171     |

<!--
## 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

#### sci_gen_colbert_triplets

* Dataset: [sci_gen_colbert_triplets](https://huggingface.co/datasets/Corran/SciGenColbertTriplets) at [44071bd](https://huggingface.co/datasets/Corran/SciGenColbertTriplets/tree/44071bdd857e9598233bd44a26a9433b46f25458)
* Size: 35,934 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                                           | positive                                                                                        | negative                                                                                         |
  |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
  | type    | string                                                                                          | string                                                                                          | string                                                                                           |
  | details | <ul><li>min: 20 characters</li><li>mean: 50.28 characters</li><li>max: 120 characters</li></ul> | <ul><li>min: 0 characters</li><li>mean: 206.53 characters</li><li>max: 401 characters</li></ul> | <ul><li>min: 96 characters</li><li>mean: 209.67 characters</li><li>max: 418 characters</li></ul> |
* Samples:
  | query                                                            | positive                                                                                                                                                                                                                                      | negative                                                                                                                                                                                                                                                                                |
  |:-----------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Previous research: highlighting negative outcomes</code>   | <code>Despite the widespread use of seniority-based wage systems in labor contracts, previous research has highlighted their negative outcomes, such as inefficiencies and demotivating effects on workers.</code>                            | <code>This paper, published in 1974, was among the first to establish the importance of rank-order tournaments as optimal labor contracts in microeconomics.</code>                                                                                                                     |
  | <code>Synthesising sources: contrasting evidence or ideas</code> | <code>Despite the observed chronic enterocolitis in Interleukin-10-deficient mice, some studies suggest that this cytokine plays a protective role in intestinal inflammation in humans (Kurimoto et al., 2001).</code>                       | <code>Chronic enterocolitis developed in Interleukin-10-deficient mice, characterized by inflammatory cell infiltration, epithelial damage, and increased production of pro-inflammatory cytokines.</code>                                                                              |
  | <code>Previous research: Approaches taken</code>                 | <code>Previous research on measuring patient-relevant outcomes in osteoarthritis has primarily relied on self-reported measures, such as the Western Ontario and McMaster Universities Arthritis Index (WOMAC) (Bellamy et al., 1988).</code> | <code>The WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) questionnaire has been widely used in physical therapy research to assess the impact of antirheumatic drug therapy on patient-reported outcomes in individuals with hip or knee osteoarthritis.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          384,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### sci_gen_colbert_triplets

* Dataset: [sci_gen_colbert_triplets](https://huggingface.co/datasets/Corran/SciGenColbertTriplets) at [44071bd](https://huggingface.co/datasets/Corran/SciGenColbertTriplets/tree/44071bdd857e9598233bd44a26a9433b46f25458)
* Size: 4,492 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                                           | positive                                                                                         | negative                                                                                         |
  |:--------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
  | type    | string                                                                                          | string                                                                                           | string                                                                                           |
  | details | <ul><li>min: 20 characters</li><li>mean: 50.59 characters</li><li>max: 120 characters</li></ul> | <ul><li>min: 98 characters</li><li>mean: 203.98 characters</li><li>max: 448 characters</li></ul> | <ul><li>min: 36 characters</li><li>mean: 204.82 characters</li><li>max: 422 characters</li></ul> |
* Samples:
  | query                                                                                | positive                                                                                                                                                                                                               | negative                                                                                                                                                                                                                                                                                                                    |
  |:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Providing background information: reference to the purpose of the study</code> | <code>This study aimed to investigate the impact of socioeconomic status on child development, specifically focusing on cognitive, language, and social-emotional domains.</code>                                      | <code>Children from high socioeconomic status families showed significantly higher IQ scores (M = 112.5, SD = 5.6) compared to children from low socioeconomic status families (M = 104.3, SD = 6.2) in the verbal IQ subtest.</code>                                                                                       |
  | <code>Providing background information: reference to the literature</code>           | <code>According to previous studies using WinGX suite for small-molecule single-crystal crystallography, the optimization of crystal structures leads to improved accuracy in determining atomic coordinates.</code>   | <code>This paper describes the WinGX suite, a powerful tool for small-molecule single-crystal crystallography that significantly advances the field of crystallography by streamlining data collection and analysis.</code>                                                                                                 |
  | <code>General comments on the relevant literature</code>                             | <code>Polymer brushes have gained significant attention in the field of polymer science due to their unique properties, such as controlled thickness, high surface density, and tunable interfacial properties.</code> | <code>Despite previous reports suggesting that polymer brushes with short grafting densities exhibit poorer performance in terms of adhesion and stability compared to those with higher grafting densities (Liu et al., 2010), our results indicate that the opposite is true for certain types of polymer brushes.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          384,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 4096
- `per_device_eval_batch_size`: 4096
- `learning_rate`: 0.02
- `num_train_epochs`: 50
- `warmup_ratio`: 0.1
- `fp16`: True

#### 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`: 4096
- `per_device_eval_batch_size`: 4096
- `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`: 0.02
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 50
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: 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`: 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`: proportional

</details>

### Training Logs
| Epoch   | Step | Training Loss | Validation Loss | SciGen-Eval-Set_cosine_ndcg@10 |
|:-------:|:----:|:-------------:|:---------------:|:------------------------------:|
| -1      | -1   | -             | -               | 0.0860                         |
| 1.1111  | 10   | 64.4072       | 61.6146         | 0.0919                         |
| 2.2222  | 20   | 60.2737       | 56.0852         | 0.1130                         |
| 3.3333  | 30   | 53.8742       | 50.1738         | 0.1611                         |
| 4.4444  | 40   | 47.9741       | 45.6099         | 0.2666                         |
| 5.5556  | 50   | 43.3533       | 42.3335         | 0.4579                         |
| 6.6667  | 60   | 39.8746       | 40.0990         | 0.6244                         |
| 7.7778  | 70   | 37.4077       | 38.4205         | 0.7223                         |
| 8.8889  | 80   | 35.3558       | 37.0939         | 0.7847                         |
| 10.0    | 90   | 33.5816       | 36.0200         | 0.8248                         |
| 11.1111 | 100  | 32.4019       | 35.1148         | 0.8469                         |
| 12.2222 | 110  | 31.3427       | 34.3602         | 0.8658                         |
| 13.3333 | 120  | 30.4578       | 33.7324         | 0.8788                         |
| 14.4444 | 130  | 29.7019       | 33.2120         | 0.8882                         |
| 15.5556 | 140  | 29.1315       | 32.7679         | 0.8963                         |
| 16.6667 | 150  | 28.6226       | 32.3942         | 0.9016                         |
| 17.7778 | 160  | 28.195        | 32.0693         | 0.9061                         |
| 18.8889 | 170  | 27.8242       | 31.7708         | 0.9096                         |
| 20.0    | 180  | 27.373        | 31.5369         | 0.9137                         |
| 21.1111 | 190  | 27.2436       | 31.3331         | 0.9168                         |
| 22.2222 | 200  | 27.0084       | 31.1571         | 0.9188                         |
| 23.3333 | 210  | 26.8023       | 31.0074         | 0.9205                         |
| 24.4444 | 220  | 26.6754       | 30.8726         | 0.9217                         |
| 25.5556 | 230  | 26.4875       | 30.7545         | 0.9224                         |
| 26.6667 | 240  | 26.3846       | 30.6494         | 0.9236                         |
| 27.7778 | 250  | 26.2546       | 30.5660         | 0.9243                         |
| 28.8889 | 260  | 26.1752       | 30.4826         | 0.9248                         |
| 30.0    | 270  | 25.9247       | 30.4060         | 0.9252                         |
| 31.1111 | 280  | 25.9807       | 30.3540         | 0.9261                         |
| 32.2222 | 290  | 25.9153       | 30.3040         | 0.9262                         |
| 33.3333 | 300  | 25.8643       | 30.2585         | 0.9265                         |
| 34.4444 | 310  | 25.7946       | 30.2183         | 0.9270                         |
| 35.5556 | 320  | 25.7723       | 30.1799         | 0.9272                         |
| 36.6667 | 330  | 25.7091       | 30.1539         | 0.9275                         |
| 37.7778 | 340  | 25.6655       | 30.1296         | 0.9275                         |
| 38.8889 | 350  | 25.6465       | 30.1120         | 0.9276                         |
| 40.0    | 360  | 25.4654       | 30.0834         | 0.9279                         |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.0
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- 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",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### 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}
}
```

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