sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:14593 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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:14593
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Macro ingredients needed to cook Poha: Orange Carrot, French Bean,
    Fresh Green Pea, Medium Poha, Red Onion, Curry Leaf, Green Chili Pepper'
  sentences:
  - Can you list recipes that contain canned chickpea and canned black bean?
  - What are the leading macro ingredients in Pigeon Pea Curry (Toor Dal)?
  - What macro ingredients form the base of Poha?
- source_sentence: 'I do have some good recommendations for you! Here are few good
    alternatives to kashmiri pulao:

    Kashmiri Dum Aloo, Shivani''s Kashmiri Dum Aloo, Chicken Pulao, Chicken Rezala,
    Chicken Kheema Masala, Hyderabadi Chicken Masala, Masala Khichdi, Lentils and
    Rice (Dal Chawal), Homestyle Vegetable Pulao'
  sentences:
  - What recipes are comparable to kashmiri pulao in flavor profile?
  - Can you give me step-by-step instructions to cook Hariyali Chicken Curry?
  - What are some recipes that utilize baking soda and olive oil effectively?
- source_sentence: 'Garnishing tip for Yellow Rice: Sprinkle with chopped cilantro.'
  sentences:
  - How can I make Yellow Rice look appealing with garnishes?
  - Describe General Tso's Tofu for me.
  - What are the best garnishing tips for Paneer Tikka Masala?
- source_sentence: 'Recipes that can be made using green chili pepper and grated coconut:
    Kerala Mix Vegetables (Aviyal), Carrot Poriyal, Cauliflower Poriyal, Beetroot
    Poriyal, Maithilee''s Fish Curry, Mix Vegetable Poriyal, Ivy Gourd Curry (Tindora
    Masala), Spiced Indian Moth Beans (Matki Usal), Fish Curry, Andhra Garlic Chicken'
  sentences:
  - What are the culinary uses of ground pork and chayote?
  - What are the dishes prepared using green cardamom and clove?
  - Can you suggest recipes that include green chili pepper and grated coconut?
- source_sentence: 'Recipes that can be made using red onion and paprika: Breakfast
    Potatoes with Sausage, Peri Peri Chicken Pasta, Scrambled Egg Curry, Chili Mac
    & Cheese, Tomato Chicken Curry'
  sentences:
  - Are there dishes that closely resemble spiced potatoes & fenugreek (aloo methi)?
  - What recipes incorporate black pepper and habanero chili in their ingredients?
  - What are some ways to use red onion and paprika in recipes?
model-index:
- name: SentenceTransformer
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 384
      type: dim_384
    metrics:
    - type: cosine_accuracy@1
      value: 0.9704069050554871
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9926017262638718
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.998766954377312
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9993834771886559
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9704069050554871
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.33086724208795726
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1997533908754624
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09993834771886559
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9704069050554871
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9926017262638718
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.998766954377312
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9993834771886559
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9865445143406266
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9822089131583582
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9822089131583582
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.9728729963008631
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9932182490752158
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.998766954377312
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9993834771886559
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9728729963008631
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3310727496917386
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1997533908754624
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09993834771886559
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9728729963008631
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9932182490752158
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.998766954377312
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9993834771886559
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9875922381599775
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9836107685984382
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9836107685984381
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.9722564734895192
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9944512946979038
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9993834771886559
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9993834771886559
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9722564734895192
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.33148376489930126
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19987669543773118
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09993834771886559
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9722564734895192
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9944512946979038
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9993834771886559
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9993834771886559
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9873346466071089
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9832511302918208
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9832511302918209
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.9704069050554871
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9944512946979038
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9993834771886559
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9993834771886559
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9704069050554871
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.33148376489930126
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19987669543773118
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09993834771886559
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9704069050554871
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9944512946979038
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9993834771886559
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9993834771886559
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9867057287670639
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9823982737361283
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9823982737361281
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 32
      type: dim_32
    metrics:
    - type: cosine_accuracy@1
      value: 0.971023427866831
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9950678175092479
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9993834771886559
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9993834771886559
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.971023427866831
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3316892725030826
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19987669543773118
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09993834771886559
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.971023427866831
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9950678175092479
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9993834771886559
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9993834771886559
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9872988931953259
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9831689272503082
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9831689272503081
      name: Cosine Map@100
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 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-v1")
# Run inference
sentences = [
    'Recipes that can be made using red onion and paprika: Breakfast Potatoes with Sausage, Peri Peri Chicken Pasta, Scrambled Egg Curry, Chili Mac & Cheese, Tomato Chicken Curry',
    'What are some ways to use red onion and paprika in recipes?',
    'Are there dishes that closely resemble spiced potatoes & fenugreek (aloo methi)?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
-->

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

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

## Evaluation

### Metrics

#### 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   | 0.9704     |
| cosine_accuracy@3   | 0.9926     |
| cosine_accuracy@5   | 0.9988     |
| cosine_accuracy@10  | 0.9994     |
| cosine_precision@1  | 0.9704     |
| cosine_precision@3  | 0.3309     |
| cosine_precision@5  | 0.1998     |
| cosine_precision@10 | 0.0999     |
| cosine_recall@1     | 0.9704     |
| cosine_recall@3     | 0.9926     |
| cosine_recall@5     | 0.9988     |
| cosine_recall@10    | 0.9994     |
| cosine_ndcg@10      | 0.9865     |
| cosine_mrr@10       | 0.9822     |
| **cosine_map@100**  | **0.9822** |

#### 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   | 0.9729     |
| cosine_accuracy@3   | 0.9932     |
| cosine_accuracy@5   | 0.9988     |
| cosine_accuracy@10  | 0.9994     |
| cosine_precision@1  | 0.9729     |
| cosine_precision@3  | 0.3311     |
| cosine_precision@5  | 0.1998     |
| cosine_precision@10 | 0.0999     |
| cosine_recall@1     | 0.9729     |
| cosine_recall@3     | 0.9932     |
| cosine_recall@5     | 0.9988     |
| cosine_recall@10    | 0.9994     |
| cosine_ndcg@10      | 0.9876     |
| cosine_mrr@10       | 0.9836     |
| **cosine_map@100**  | **0.9836** |

#### 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   | 0.9723     |
| cosine_accuracy@3   | 0.9945     |
| cosine_accuracy@5   | 0.9994     |
| cosine_accuracy@10  | 0.9994     |
| cosine_precision@1  | 0.9723     |
| cosine_precision@3  | 0.3315     |
| cosine_precision@5  | 0.1999     |
| cosine_precision@10 | 0.0999     |
| cosine_recall@1     | 0.9723     |
| cosine_recall@3     | 0.9945     |
| cosine_recall@5     | 0.9994     |
| cosine_recall@10    | 0.9994     |
| cosine_ndcg@10      | 0.9873     |
| cosine_mrr@10       | 0.9833     |
| **cosine_map@100**  | **0.9833** |

#### 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   | 0.9704     |
| cosine_accuracy@3   | 0.9945     |
| cosine_accuracy@5   | 0.9994     |
| cosine_accuracy@10  | 0.9994     |
| cosine_precision@1  | 0.9704     |
| cosine_precision@3  | 0.3315     |
| cosine_precision@5  | 0.1999     |
| cosine_precision@10 | 0.0999     |
| cosine_recall@1     | 0.9704     |
| cosine_recall@3     | 0.9945     |
| cosine_recall@5     | 0.9994     |
| cosine_recall@10    | 0.9994     |
| cosine_ndcg@10      | 0.9867     |
| cosine_mrr@10       | 0.9824     |
| **cosine_map@100**  | **0.9824** |

#### Information Retrieval
* Dataset: `dim_32`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.971      |
| cosine_accuracy@3   | 0.9951     |
| cosine_accuracy@5   | 0.9994     |
| cosine_accuracy@10  | 0.9994     |
| cosine_precision@1  | 0.971      |
| cosine_precision@3  | 0.3317     |
| cosine_precision@5  | 0.1999     |
| cosine_precision@10 | 0.0999     |
| cosine_recall@1     | 0.971      |
| cosine_recall@3     | 0.9951     |
| cosine_recall@5     | 0.9994     |
| cosine_recall@10    | 0.9994     |
| cosine_ndcg@10      | 0.9873     |
| cosine_mrr@10       | 0.9832     |
| **cosine_map@100**  | **0.9832** |

<!--
## 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: 14,593 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: 53.46 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.83 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                   | anchor                                                                            |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | <code>Calories information of Hyderabadi Chicken Masala, based on different serving sizes: Serving 1 - 345 calories, Serving 2 - 580 calories, Serving 3 - 1220 calories, Serving 4 - 1450 calories</code> | <code>What’s the calorie content of Hyderabadi Chicken Masala?</code>             |
  | <code>Recipes that can be made using dried herb mix and onion powder: Chorizo Queso Soup, Cheesy Chicken & Broccoli</code>                                                                                 | <code>What are some food items made using dried herb mix and onion powder?</code> |
  | <code>Recipes that can be made using roasted semolina/bombay rava and saffron: Rashmi's Kesari Bath, Pineapple Kesari Bath</code>                                                                          | <code>What recipes have roasted semolina/bombay rava and saffron in them?</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,
          32
      ],
      "matryoshka_weights": [
          1,
          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`: 32
- `gradient_accumulation_steps`: 16
- `learning_rate`: 1e-05
- `num_train_epochs`: 20
- `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`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 1e-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`: 20
- `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_32_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 |
|:-------:|:----:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:|
| 0.3501  | 10   | 0.0066        | -                      | -                      | -                     | -                      | -                     |
| 0.7002  | 20   | 0.0056        | -                      | -                      | -                     | -                      | -                     |
| 0.9803  | 28   | -             | 0.9746                 | 0.9771                 | 0.9776                | 0.9758                 | 0.9763                |
| 1.0503  | 30   | 0.0057        | -                      | -                      | -                     | -                      | -                     |
| 1.4004  | 40   | 0.0048        | -                      | -                      | -                     | -                      | -                     |
| 1.7505  | 50   | 0.0039        | -                      | -                      | -                     | -                      | -                     |
| 1.9956  | 57   | -             | 0.9783                 | 0.9787                 | 0.9815                | 0.9788                 | 0.9793                |
| 2.1007  | 60   | 0.0046        | -                      | -                      | -                     | -                      | -                     |
| 2.4508  | 70   | 0.0035        | -                      | -                      | -                     | -                      | -                     |
| 2.8009  | 80   | 0.0028        | -                      | -                      | -                     | -                      | -                     |
| 2.9759  | 85   | -             | 0.9818                 | 0.9811                 | 0.9836                | 0.9803                 | 0.9823                |
| 3.1510  | 90   | 0.0036        | -                      | -                      | -                     | -                      | -                     |
| 3.5011  | 100  | 0.0033        | -                      | -                      | -                     | -                      | -                     |
| 3.8512  | 110  | 0.0026        | -                      | -                      | -                     | -                      | -                     |
| 3.9912  | 114  | -             | 0.9814                 | 0.9818                 | 0.9844                | 0.9814                 | 0.9821                |
| 4.2013  | 120  | 0.0025        | -                      | -                      | -                     | -                      | -                     |
| 4.5514  | 130  | 0.003         | -                      | -                      | -                     | -                      | -                     |
| 4.9015  | 140  | 0.0027        | -                      | -                      | -                     | -                      | -                     |
| 4.9716  | 142  | -             | 0.9825                 | 0.9819                 | 0.9844                | 0.9823                 | 0.9825                |
| 5.2516  | 150  | 0.0024        | -                      | -                      | -                     | -                      | -                     |
| 5.6018  | 160  | 0.0023        | -                      | -                      | -                     | -                      | -                     |
| 5.9519  | 170  | 0.0024        | -                      | -                      | -                     | -                      | -                     |
| 5.9869  | 171  | -             | 0.9831                 | 0.9826                 | 0.9846                | 0.9818                 | 0.9831                |
| 6.3020  | 180  | 0.0025        | -                      | -                      | -                     | -                      | -                     |
| 6.6521  | 190  | 0.0025        | -                      | -                      | -                     | -                      | -                     |
| 6.9672  | 199  | -             | 0.9830                 | 0.9825                 | 0.9844                | 0.9823                 | 0.9831                |
| 7.0022  | 200  | 0.0019        | -                      | -                      | -                     | -                      | -                     |
| 7.3523  | 210  | 0.0022        | -                      | -                      | -                     | -                      | -                     |
| 7.7024  | 220  | 0.0026        | -                      | -                      | -                     | -                      | -                     |
| 7.9825  | 228  | -             | 0.9828                 | 0.9825                 | 0.9836                | 0.9821                 | 0.9821                |
| 8.0525  | 230  | 0.0022        | -                      | -                      | -                     | -                      | -                     |
| 8.4026  | 240  | 0.0021        | -                      | -                      | -                     | -                      | -                     |
| 8.7527  | 250  | 0.0021        | -                      | -                      | -                     | -                      | -                     |
| 8.9978  | 257  | -             | 0.9827                 | 0.9826                 | 0.9848                | 0.9827                 | 0.9827                |
| 9.1028  | 260  | 0.0025        | -                      | -                      | -                     | -                      | -                     |
| 9.4530  | 270  | 0.0022        | -                      | -                      | -                     | -                      | -                     |
| 9.8031  | 280  | 0.0019        | -                      | -                      | -                     | -                      | -                     |
| 9.9781  | 285  | -             | 0.9832                 | 0.9833                 | 0.9858                | 0.9825                 | 0.9834                |
| 10.1532 | 290  | 0.0021        | -                      | -                      | -                     | -                      | -                     |
| 10.5033 | 300  | 0.0019        | -                      | -                      | -                     | -                      | -                     |
| 10.8534 | 310  | 0.0024        | -                      | -                      | -                     | -                      | -                     |
| 10.9934 | 314  | -             | 0.9830                 | 0.9827                 | 0.9850                | 0.9825                 | 0.9829                |
| 11.2035 | 320  | 0.0017        | -                      | -                      | -                     | -                      | -                     |
| 11.5536 | 330  | 0.0017        | -                      | -                      | -                     | -                      | -                     |
| 11.9037 | 340  | 0.0018        | -                      | -                      | -                     | -                      | -                     |
| 11.9737 | 342  | -             | 0.9827                 | 0.9835                 | 0.9841                | 0.9826                 | 0.9827                |
| 12.2538 | 350  | 0.0018        | -                      | -                      | -                     | -                      | -                     |
| 12.6039 | 360  | 0.0018        | -                      | -                      | -                     | -                      | -                     |
| 12.9540 | 370  | 0.0023        | -                      | -                      | -                     | -                      | -                     |
| 12.9891 | 371  | -             | 0.9828                 | 0.9834                 | 0.9832                | 0.9826                 | 0.9823                |
| 13.3042 | 380  | 0.0017        | -                      | -                      | -                     | -                      | -                     |
| 13.6543 | 390  | 0.0018        | -                      | -                      | -                     | -                      | -                     |
| 13.9694 | 399  | -             | 0.9830                 | 0.9831                 | 0.9838                | 0.9820                 | 0.9826                |
| 14.0044 | 400  | 0.0016        | -                      | -                      | -                     | -                      | -                     |
| 14.3545 | 410  | 0.0018        | -                      | -                      | -                     | -                      | -                     |
| 14.7046 | 420  | 0.0018        | -                      | -                      | -                     | -                      | -                     |
| 14.9847 | 428  | -             | 0.9827                 | 0.9825                 | 0.9832                | 0.9816                 | 0.9826                |
| 15.0547 | 430  | 0.0018        | -                      | -                      | -                     | -                      | -                     |
| 15.4048 | 440  | 0.0015        | -                      | -                      | -                     | -                      | -                     |
| 15.7549 | 450  | 0.0017        | -                      | -                      | -                     | -                      | -                     |
| 16.0    | 457  | -             | 0.9833                 | 0.9836                 | 0.9832                | 0.9822                 | 0.9824                |


### 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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