README.md

---
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:42333
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Tag: chicken & broccoli alfredo


    For chicken & broccoli alfredo, these dietary tags go well with it: dinner, italian
    cuisine, meat recipes, lunch, italian american cuisine, american cuisine, pasta
    recipes, contains dairy, european cuisine, vegetarian'
  sentences:
  - 'Tag: chicken & broccoli alfredo


    What dietary classifications are suitable for chicken & broccoli alfredo?'
  - 'Tag: vegan pad thai


    What dietary labels fit vegan pad thai?'
  - 'Tag: apple pie filling


    Which dietary tags apply to apple pie filling?'
- source_sentence: 'Tag: beef and broccoli


    A small description of beef and broccoli: Stir fried broccoli and tender beef
    strips stir-fried in a rich savory sauce.'
  sentences:
  - 'Tag: chicken lettuce wrap


    What are the principal macro ingredients of chicken lettuce wrap?'
  - 'Tag: teriyaki tofu


    What are the micro ingredients used in teriyaki tofu?'
  - 'Tag: beef and broccoli


    What’s the best way to describe beef and broccoli?'
- source_sentence: 'Tag: scrambled eggs with veggies


    For scrambled eggs with veggies, these dietary tags go well with it: breakfast,
    american cuisine, protein rich recipes, stir fry recipes, gluten free recipes'
  sentences:
  - 'Tag: kimchi fried rice (chicken)


    What are the vital macro ingredients in kimchi fried rice (chicken)?'
  - 'Tag: scrambled eggs with veggies


    What are the key macro ingredients for scrambled eggs with veggies?'
  - 'Tag: scrambled eggs with veggies


    How should I label scrambled eggs with veggies in terms of dietary categories?'
- source_sentence: 'Tag: mixed vegetable stir-fry


    Micro ingredients required to cook mixed vegetable stir-fry:

    Salt, Cornstarch, Black Pepper Powder'
  sentences:
  - 'Tag: vegan pad thai


    Can you provide a thorough explanation of how to cook vegan pad thai?'
  - 'Tag: chicken & broccoli alfredo


    What’s involved in preparing the ingredients for chicken & broccoli alfredo?'
  - 'Tag: mixed vegetable stir-fry


    What are the main components of mixed vegetable stir-fry?'
- source_sentence: 'Tag: vegan pad thai


    Cook time of vegan pad thai based on different serving sizes: Serving 1 - 20 mins,
    Serving 2 - 25 mins, Serving 3 - 30 mins, Serving 4 - 35 mins'
  sentences:
  - 'Tag: vegan pad thai


    What’s the expected cook time for vegan pad thai?'
  - 'Tag: scrambled eggs with veggies


    What dietary classifications suit scrambled eggs with veggies?'
  - 'Tag: vegetable pulao


    What are some creative garnishing tips for vegetable pulao?'
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: 0.9688300597779675
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9701110162254484
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9748078565328779
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9946626814688301
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9688300597779675
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.8469968687731283
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.8014944491887276
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.4411614005123826
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3285582123541133
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6209009393680616
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8938791122768492
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9605094343458989
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9592536302802654
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9733707623385245
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9539794228951505
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.9679760888129804
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9692570452604612
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9752348420153715
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9948761742100769
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9679760888129804
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.8459294050668943
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.7992741246797609
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.43917591801878736
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.32842427107478345
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6204243930706356
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8918949005733805
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9569316518238379
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9566533189656364
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9727438392094664
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9511517923410544
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.9694705380017079
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9705380017079419
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9760888129803587
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9948761742100769
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9694705380017079
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.8471391972672928
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.798462852263023
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.43800170794193005
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3286967284778983
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6210852039363994
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8912874628929282
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9550379203773738
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9558695124747556
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9739451594756885
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9499982560169666
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.9698975234842016
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9720324508966696
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9771562766865927
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9938087105038429
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9698975234842016
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.8472815257614573
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.7965841161400511
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.4339666951323655
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3288006791102436
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.621300984099874
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.889481670123216
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9478284738318897
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9517343805870713
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.974398746831496
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9459942940005901
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 32
      type: dim_32
    metrics:
    - type: cosine_accuracy@1
      value: 0.9690435525192144
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9707514944491887
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9769427839453458
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9929547395388557
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9690435525192144
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.8464987190435526
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.7940222032450898
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.4318531169940221
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3286197185962344
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6208008011060958
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8871009719002887
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9440570228945548
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9489614439178549
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9734810669215016
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9417483259746888
      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-v2")
# Run inference
sentences = [
    'Tag: vegan pad thai\n\nCook time of vegan pad thai based on different serving sizes: Serving 1 - 20 mins, Serving 2 - 25 mins, Serving 3 - 30 mins, Serving 4 - 35 mins',
    'Tag: vegan pad thai\n\nWhat’s the expected cook time for vegan pad thai?',
    'Tag: scrambled eggs with veggies\n\nWhat dietary classifications suit scrambled eggs with veggies?',
]
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   | 0.9688    |
| cosine_accuracy@3   | 0.9701    |
| cosine_accuracy@5   | 0.9748    |
| cosine_accuracy@10  | 0.9947    |
| cosine_precision@1  | 0.9688    |
| cosine_precision@3  | 0.847     |
| cosine_precision@5  | 0.8015    |
| cosine_precision@10 | 0.4412    |
| cosine_recall@1     | 0.3286    |
| cosine_recall@3     | 0.6209    |
| cosine_recall@5     | 0.8939    |
| cosine_recall@10    | 0.9605    |
| cosine_ndcg@10      | 0.9593    |
| cosine_mrr@10       | 0.9734    |
| **cosine_map@100**  | **0.954** |

#### 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   | 0.968      |
| cosine_accuracy@3   | 0.9693     |
| cosine_accuracy@5   | 0.9752     |
| cosine_accuracy@10  | 0.9949     |
| cosine_precision@1  | 0.968      |
| cosine_precision@3  | 0.8459     |
| cosine_precision@5  | 0.7993     |
| cosine_precision@10 | 0.4392     |
| cosine_recall@1     | 0.3284     |
| cosine_recall@3     | 0.6204     |
| cosine_recall@5     | 0.8919     |
| cosine_recall@10    | 0.9569     |
| cosine_ndcg@10      | 0.9567     |
| cosine_mrr@10       | 0.9727     |
| **cosine_map@100**  | **0.9512** |

#### 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   | 0.9695   |
| cosine_accuracy@3   | 0.9705   |
| cosine_accuracy@5   | 0.9761   |
| cosine_accuracy@10  | 0.9949   |
| cosine_precision@1  | 0.9695   |
| cosine_precision@3  | 0.8471   |
| cosine_precision@5  | 0.7985   |
| cosine_precision@10 | 0.438    |
| cosine_recall@1     | 0.3287   |
| cosine_recall@3     | 0.6211   |
| cosine_recall@5     | 0.8913   |
| cosine_recall@10    | 0.955    |
| cosine_ndcg@10      | 0.9559   |
| cosine_mrr@10       | 0.9739   |
| **cosine_map@100**  | **0.95** |

#### 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   | 0.9699    |
| cosine_accuracy@3   | 0.972     |
| cosine_accuracy@5   | 0.9772    |
| cosine_accuracy@10  | 0.9938    |
| cosine_precision@1  | 0.9699    |
| cosine_precision@3  | 0.8473    |
| cosine_precision@5  | 0.7966    |
| cosine_precision@10 | 0.434     |
| cosine_recall@1     | 0.3288    |
| cosine_recall@3     | 0.6213    |
| cosine_recall@5     | 0.8895    |
| cosine_recall@10    | 0.9478    |
| cosine_ndcg@10      | 0.9517    |
| cosine_mrr@10       | 0.9744    |
| **cosine_map@100**  | **0.946** |

#### 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   | 0.969      |
| cosine_accuracy@3   | 0.9708     |
| cosine_accuracy@5   | 0.9769     |
| cosine_accuracy@10  | 0.993      |
| cosine_precision@1  | 0.969      |
| cosine_precision@3  | 0.8465     |
| cosine_precision@5  | 0.794      |
| cosine_precision@10 | 0.4319     |
| cosine_recall@1     | 0.3286     |
| cosine_recall@3     | 0.6208     |
| cosine_recall@5     | 0.8871     |
| cosine_recall@10    | 0.9441     |
| cosine_ndcg@10      | 0.949      |
| cosine_mrr@10       | 0.9735     |
| **cosine_map@100**  | **0.9417** |

<!--
## 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: 42,333 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: 17 tokens</li><li>mean: 71.13 tokens</li><li>max: 433 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 22.97 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                      | anchor                                                                                            |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|
  | <code>Tag: beef and broccoli<br><br>A small description of beef and broccoli: Stir fried broccoli and tender beef strips stir-fried in a rich savory sauce.</code>                                                                                                                                                                                            | <code>Tag: beef and broccoli<br><br>How do you describe beef and broccoli?</code>                 |
  | <code>Tag: beef and broccoli<br><br>Garnishing tips for beef and broccoli: Best served on it's own or on top of hot rice with chopped scallions!</code>                                                                                                                                                                                                       | <code>Tag: beef and broccoli<br><br>What are some classic garnishes for beef and broccoli?</code> |
  | <code>Tag: beef and broccoli<br><br>For beef and broccoli, these dietary tags go well with it: dinner, contains soy, meat recipes, asian american cuisine, lunch, american cuisine, beef recipes, asian cuisine, chinese cuisine, hearty recipes, rice recipes, protein rich recipes, non vegetarian, saucy recipes, stir fry recipes, healthy recipes</code> | <code>Tag: beef and broccoli<br><br>What dietary labels suit beef and broccoli?</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`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 100
- `lr_scheduler_type`: constant
- `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`: steps
- `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`: 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`: 100
- `max_steps`: -1
- `lr_scheduler_type`: constant
- `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 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:|
| 0.3023 | 25   | 2.7893        | 0.9106                 | 0.9169                 | 0.8833                | 0.9193                 | 0.9013                |
| 0.6047 | 50   | 1.6554        | 0.9061                 | 0.9153                 | 0.8858                | 0.9199                 | 0.8970                |
| 0.9070 | 75   | 0.7514        | 0.9361                 | 0.9382                 | 0.9216                | 0.9423                 | 0.9292                |
| 1.2079 | 100  | 1.2044        | 0.9334                 | 0.9370                 | 0.9186                | 0.9413                 | 0.9263                |
| 1.5102 | 125  | 1.4103        | 0.9312                 | 0.9342                 | 0.9146                | 0.9382                 | 0.9222                |
| 1.8125 | 150  | 0.6925        | 0.9444                 | 0.9463                 | 0.9326                | 0.9502                 | 0.9385                |
| 2.1134 | 175  | 0.7937        | 0.9333                 | 0.9376                 | 0.9196                | 0.9410                 | 0.9256                |
| 2.4157 | 200  | 1.3185        | 0.9321                 | 0.9355                 | 0.9191                | 0.9399                 | 0.9245                |
| 2.7181 | 225  | 1.0296        | 0.9400                 | 0.9426                 | 0.9293                | 0.9466                 | 0.9345                |
| 3.0189 | 250  | 0.3606        | 0.9342                 | 0.9373                 | 0.9231                | 0.9417                 | 0.9282                |
| 3.3212 | 275  | 1.2364        | 0.9381                 | 0.9410                 | 0.9273                | 0.9444                 | 0.9312                |
| 3.6236 | 300  | 1.2507        | 0.9305                 | 0.9340                 | 0.9193                | 0.9385                 | 0.9233                |
| 3.9259 | 325  | 0.3211        | 0.9500                 | 0.9512                 | 0.9417                | 0.9540                 | 0.9460                |


### 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->