---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4247
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
widget:
- source_sentence: Perry syndrome is a familial parkinsonism associated with central
    hypoventilation, mental depression, and weight loss.
  sentences:
  - List features of the Perry syndrome.
  - Which is the main abnormality that arises with Sox9 locus duplication?
  - Was modafinil tested for schizophrenia treatment?
- source_sentence: Yes. HDAC1 is required for GATA-1 transcription activity, global
    chromatin occupancy and hematopoiesis.
  sentences:
  - Is HDAC1 required for GATA-1 transcriptional activity?
  - Which cells are affected in radiation-induced leukemias?
  - Is phospholamban phosphorylated by Protein kinase A?
- source_sentence: Long noncoding RNAs (lncRNAs) constitute the majority of transcripts
    in the mammalian genomes, and yet, their functions remain largely unknown. As
    part of the FANTOM6 project, the expression of 285 lncRNAs was systematically
    knocked down in human dermal fibroblasts. Cellular growth, morphological changes,
    and transcriptomic responses were quantified using Capped Analysis of Gene Expression
    (CAGE).The functional annotation of the mammalian genome 6 (FANTOM6) project aims
    to systematically map all human long noncoding RNAs (lncRNAs) in a gene-dependent
    manner through dedicated efforts from national and international teams
  sentences:
  - What delivery system is used for the Fluzone Intradermal vaccine?
  - What is dovitinib?
  - Which class of genomic elements was assessed as part of the FANTOM6 project?
- source_sentence: ' The proband had normal molecular analysis of the glypican 6 gene
    (GPC6), which was recently reported as a candidate for autosomal recessive omodysplasiaThe
    proband had normal molecular analysis of the glypican 6 gene (GPC6), which was
    recently reported as a candidate for autosomal recessive omodysplasiaThe glypican
    6 gene (GPC6), which was recently reported as a candidate for autosomal recessive
    omodysplasia.Omodysplasia is a rare autosomal recessive disorder with a frequency
    of 1 in 50,000 newborn, and is associated with mutations in the GPC6 gene on chromosome
    13.'
  sentences:
  - What is the effect of ivabradine in heart failure with preserved ejection fraction?
  - What rare disease is associated with a mutation in the GPC6 gene on chromosome
    13?
  - What is the effect of rHDL-apoE3 on endothelial cell migration?
- source_sentence: Yes, numerous whole exome sequencing studies of ALzheimer patients
    have been conducted.
  sentences:
  - Is muscle regeneration possible in mdx mice with the use of induced mesenchymal
    stem cells?
  - Has whole exome sequencing been performed in Alzheimer patients?
  - How is connected "isolated Non-compaction cardiomyopathy" with dilated cardiomyopathy?
pipeline_tag: sentence-similarity
model-index:
- name: BGE base BioASQ Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.8516949152542372
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.940677966101695
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9576271186440678
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.961864406779661
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8516949152542372
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31355932203389825
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19152542372881357
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09618644067796611
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8516949152542372
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.940677966101695
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9576271186440678
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.961864406779661
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9149563623470877
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8990348399246703
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8999167242053622
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.8516949152542372
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9449152542372882
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9555084745762712
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9597457627118644
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8516949152542372
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3149717514124293
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19110169491525428
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09597457627118645
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8516949152542372
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9449152542372882
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9555084745762712
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9597457627118644
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9136223756024043
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8979166666666664
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8990624087448101
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.8389830508474576
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.934322033898305
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9470338983050848
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9597457627118644
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8389830508474576
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3114406779661017
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.189406779661017
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09597457627118645
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8389830508474576
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.934322033898305
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9470338983050848
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9597457627118644
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9053426368336166
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8872721616895344
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8879933659912613
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.8241525423728814
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9110169491525424
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9322033898305084
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9470338983050848
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8241525423728814
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.30367231638418074
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1864406779661017
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09470338983050848
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8241525423728814
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9110169491525424
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9322033898305084
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9470338983050848
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8905411432220106
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8719422585418346
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8732028981082185
      name: Cosine Map@100
---

# BGE base BioASQ Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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': 768, '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})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("pavanmantha/bge-base-en-bioembed")
# Run inference
sentences = [
    'Yes, numerous whole exome sequencing studies of ALzheimer patients have been conducted.',
    'Has whole exome sequencing been performed in Alzheimer patients?',
    'How is connected "isolated Non-compaction cardiomyopathy" with dilated cardiomyopathy?',
]
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>
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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8517     |
| cosine_accuracy@3   | 0.9407     |
| cosine_accuracy@5   | 0.9576     |
| cosine_accuracy@10  | 0.9619     |
| cosine_precision@1  | 0.8517     |
| cosine_precision@3  | 0.3136     |
| cosine_precision@5  | 0.1915     |
| cosine_precision@10 | 0.0962     |
| cosine_recall@1     | 0.8517     |
| cosine_recall@3     | 0.9407     |
| cosine_recall@5     | 0.9576     |
| cosine_recall@10    | 0.9619     |
| cosine_ndcg@10      | 0.915      |
| cosine_mrr@10       | 0.899      |
| **cosine_map@100**  | **0.8999** |

#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8517     |
| cosine_accuracy@3   | 0.9449     |
| cosine_accuracy@5   | 0.9555     |
| cosine_accuracy@10  | 0.9597     |
| cosine_precision@1  | 0.8517     |
| cosine_precision@3  | 0.315      |
| cosine_precision@5  | 0.1911     |
| cosine_precision@10 | 0.096      |
| cosine_recall@1     | 0.8517     |
| cosine_recall@3     | 0.9449     |
| cosine_recall@5     | 0.9555     |
| cosine_recall@10    | 0.9597     |
| cosine_ndcg@10      | 0.9136     |
| cosine_mrr@10       | 0.8979     |
| **cosine_map@100**  | **0.8991** |

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| cosine_accuracy@1   | 0.839     |
| cosine_accuracy@3   | 0.9343    |
| cosine_accuracy@5   | 0.947     |
| cosine_accuracy@10  | 0.9597    |
| cosine_precision@1  | 0.839     |
| cosine_precision@3  | 0.3114    |
| cosine_precision@5  | 0.1894    |
| cosine_precision@10 | 0.096     |
| cosine_recall@1     | 0.839     |
| cosine_recall@3     | 0.9343    |
| cosine_recall@5     | 0.947     |
| cosine_recall@10    | 0.9597    |
| cosine_ndcg@10      | 0.9053    |
| cosine_mrr@10       | 0.8873    |
| **cosine_map@100**  | **0.888** |

#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8242     |
| cosine_accuracy@3   | 0.911      |
| cosine_accuracy@5   | 0.9322     |
| cosine_accuracy@10  | 0.947      |
| cosine_precision@1  | 0.8242     |
| cosine_precision@3  | 0.3037     |
| cosine_precision@5  | 0.1864     |
| cosine_precision@10 | 0.0947     |
| cosine_recall@1     | 0.8242     |
| cosine_recall@3     | 0.911      |
| cosine_recall@5     | 0.9322     |
| cosine_recall@10    | 0.947      |
| cosine_ndcg@10      | 0.8905     |
| cosine_mrr@10       | 0.8719     |
| **cosine_map@100**  | **0.8732** |

<!--
## Bias, Risks and Limitations

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 4,247 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                            | anchor                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 103.25 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.94 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                        | anchor                                                                                                 |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|
  | <code>Yes, saracatinib is being studied as a treatment against Alzheimer's Disease. A clinical Phase Ib study has been completed, and a clinical Phase IIa study is ongoing.</code>                                                             | <code>Was saracatinib being considered as a treatment for Alzheimer's disease in November 2017?</code> |
  | <code>TREM2 variants have been found to be associated with early as well as with late onset Alzheimer's disease.</code>                                                                                                                         | <code>Is TREM2 associated with Alzheimer's disease in humans?</code>                                   |
  | <code>Yes, siltuximab , a chimeric human-mouse monoclonal antibody to IL6, is approved for the treatment of patients with multicentric Castleman disease who are human immunodeficiency virus negative and human herpesvirus-8 negative.</code> | <code>Is siltuximab effective for Castleman disease?</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,
          512,
          256,
          128
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `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`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: False
- `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`: True
- `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_fused
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step  | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|
| **0.9624** | **8** | **-**         | **0.8794**             | **0.8937**             | **0.9044**             | **0.9018**             |
| 1.2030     | 10    | 1.1405        | -                      | -                      | -                      | -                      |
| 1.9248     | 16    | -             | 0.8739                 | 0.8866                 | 0.8998                 | 0.8984                 |
| 2.4060     | 20    | 0.4328        | -                      | -                      | -                      | -                      |
| 2.8872     | 24    | -             | 0.8732                 | 0.8876                 | 0.8987                 | 0.8998                 |
| 3.6090     | 30    | 0.312         | -                      | -                      | -                      | -                      |
| 3.8496     | 32    | -             | 0.8732                 | 0.8880                 | 0.8991                 | 0.8999                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.31.0
- Datasets: 2.19.1
- 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",
}
```

#### 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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