---
base_model: BAAI/bge-small-en-v1.5
datasets: []
language: []
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:60323
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: No recipes found with these beef stock powder and orange juice!
  sentences:
  - Can you provide recipe ideas with beef stock powder and orange juice?
  - What are some recipes that utilize jasmine rice and thai red curry paste effectively?
  - What recipes incorporate broccoli and bacon into meals?
- source_sentence: No recipes found with these nutmeg flower and angel hair rice noodles!
  sentences:
  - What dishes can be created with kale and bok choy?
  - What recipes incorporate green zucchini and vegan ground beef into meals?
  - Can you provide me with meal ideas using nutmeg flower and angel hair rice noodles?
- source_sentence: No recipes found with these cinnamon and ground lamb!
  sentences:
  - Can you suggest dishes where cinnamon and ground lamb is key?
  - What diet tags are relevant to Sneha's Aloo Baingan ?
  - What recipes are there with toasted sesame oil and red lentils/masoor?
- source_sentence: No recipes found with these red lentils/masoor and bok choy!
  sentences:
  - What are the culinary uses of chili sauce and sriracha?
  - What are some ways to use canned tomato puree and frozen ube in recipes?
  - What are some ideas for dishes with red lentils/masoor and bok choy?
- source_sentence: No recipes found with these red onion and cubed stuffing!
  sentences:
  - Can you provide meal suggestions involving vanilla extract and brown lentil/black
    masoor dal?
  - What recipes incorporate methi (fenugreek) and honey in their ingredients?
  - What culinary preparations can be made with red onion and cubed stuffing?
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 384
      type: dim_384
    metrics:
    - type: cosine_accuracy@1
      value: 0.9819483813217962
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9976130091004028
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9995524392063255
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9819483813217962
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.33253766970013426
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1999104878412651
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9819483813217962
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9976130091004028
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9995524392063255
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9923670621371893
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9897597379993318
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9897597379993323
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.9812024466656721
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.997463822169178
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9998508130687752
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9812024466656721
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3324879407230593
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19997016261375503
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9812024466656721
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.997463822169178
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9998508130687752
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9921395779775503
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9894450246158434
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9894450246158436
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.979561390422199
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9970162613755035
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9998508130687752
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.979561390422199
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3323387537918345
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19997016261375505
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.979561390422199
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9970162613755035
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9998508130687752
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9913010184783637
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9883310955293644
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9883310955293649
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.9816500074593466
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9968670744442787
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9997016261375503
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9816500074593466
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3322890248147595
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19994032522751004
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9816500074593466
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9968670744442787
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9997016261375503
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9920343842432707
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9893333120209138
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9893333120209146
      name: Cosine Map@100
---

# SentenceTransformer based on BAAI/bge-small-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("Adi-0-0-Gupta/Embedding")
# Run inference
sentences = [
    'No recipes found with these red onion and cubed stuffing!',
    'What culinary preparations can be made with red onion and cubed stuffing?',
    'Can you provide meal suggestions involving vanilla extract and brown lentil/black masoor dal?',
]
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.*
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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9819     |
| cosine_accuracy@3   | 0.9976     |
| cosine_accuracy@5   | 0.9996     |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9819     |
| cosine_precision@3  | 0.3325     |
| cosine_precision@5  | 0.1999     |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9819     |
| cosine_recall@3     | 0.9976     |
| cosine_recall@5     | 0.9996     |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9924     |
| cosine_mrr@10       | 0.9898     |
| **cosine_map@100**  | **0.9898** |

#### 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   | 0.9812     |
| cosine_accuracy@3   | 0.9975     |
| cosine_accuracy@5   | 0.9999     |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9812     |
| cosine_precision@3  | 0.3325     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9812     |
| cosine_recall@3     | 0.9975     |
| cosine_recall@5     | 0.9999     |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9921     |
| cosine_mrr@10       | 0.9894     |
| **cosine_map@100**  | **0.9894** |

#### 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   | 0.9796     |
| cosine_accuracy@3   | 0.997      |
| cosine_accuracy@5   | 0.9999     |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9796     |
| cosine_precision@3  | 0.3323     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9796     |
| cosine_recall@3     | 0.997      |
| cosine_recall@5     | 0.9999     |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9913     |
| cosine_mrr@10       | 0.9883     |
| **cosine_map@100**  | **0.9883** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9817     |
| cosine_accuracy@3   | 0.9969     |
| cosine_accuracy@5   | 0.9997     |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9817     |
| cosine_precision@3  | 0.3323     |
| cosine_precision@5  | 0.1999     |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9817     |
| cosine_recall@3     | 0.9969     |
| cosine_recall@5     | 0.9997     |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.992      |
| cosine_mrr@10       | 0.9893     |
| **cosine_map@100**  | **0.9893** |

<!--
## 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: 60,323 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: 11 tokens</li><li>mean: 21.41 tokens</li><li>max: 503 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 16.8 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
  | positive                                                                                  | anchor                                                                                            |
  |:------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|
  | <code>No recipes found with these indian cottage cheese (paneer) and bitter melon!</code> | <code>What are some culinary options with indian cottage cheese (paneer) and bitter melon?</code> |
  | <code>No recipes found with these curry leaf and rice cakes!</code>                       | <code>What recipes can be made using curry leaf and rice cakes?</code>                            |
  | <code>No recipes found with these bacon and  rosemary!</code>                             | <code>What are the different culinary recipes that use bacon and  rosemary?</code>                |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `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`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `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`: 10
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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_384_cosine_map@100 | dim_64_cosine_map@100 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.0848 | 10   | 3.9258        | -                      | -                      | -                      | -                     |
| 0.1697 | 20   | 3.0513        | -                      | -                      | -                      | -                     |
| 0.2545 | 30   | 1.6368        | -                      | -                      | -                      | -                     |
| 0.3393 | 40   | 0.5491        | -                      | -                      | -                      | -                     |
| 0.4242 | 50   | 0.1541        | -                      | -                      | -                      | -                     |
| 0.5090 | 60   | 0.0615        | -                      | -                      | -                      | -                     |
| 0.5938 | 70   | 0.0426        | -                      | -                      | -                      | -                     |
| 0.6787 | 80   | 0.037         | -                      | -                      | -                      | -                     |
| 0.7635 | 90   | 0.0312        | -                      | -                      | -                      | -                     |
| 0.8484 | 100  | 0.0246        | -                      | -                      | -                      | -                     |
| 0.9332 | 110  | 0.029         | -                      | -                      | -                      | -                     |
| 0.9926 | 117  | -             | 0.9855                 | 0.9869                 | 0.9869                 | 0.9855                |
| 1.0180 | 120  | 0.0205        | -                      | -                      | -                      | -                     |
| 1.1029 | 130  | 0.0212        | -                      | -                      | -                      | -                     |
| 1.1877 | 140  | 0.0196        | -                      | -                      | -                      | -                     |
| 1.2725 | 150  | 0.0157        | -                      | -                      | -                      | -                     |
| 1.3574 | 160  | 0.0174        | -                      | -                      | -                      | -                     |
| 1.4422 | 170  | 0.0152        | -                      | -                      | -                      | -                     |
| 1.5270 | 180  | 0.0155        | -                      | -                      | -                      | -                     |
| 1.6119 | 190  | 0.0133        | -                      | -                      | -                      | -                     |
| 1.6967 | 200  | 0.0173        | -                      | -                      | -                      | -                     |
| 1.7815 | 210  | 0.014         | -                      | -                      | -                      | -                     |
| 1.8664 | 220  | 0.0127        | -                      | -                      | -                      | -                     |
| 1.9512 | 230  | 0.0116        | -                      | -                      | -                      | -                     |
| 1.9936 | 235  | -             | 0.9883                 | 0.9894                 | 0.9898                 | 0.9893                |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- 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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