sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:9600 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
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:9600
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: The median home value in San Carlos, CA is $2,350,000.
  sentences:
  - What does the console property of the WorkerGlobalScope interface provide access
    to?
  - What is the last sold price and date for the property at 4372 W 14th Street Dr,
    Greeley, CO 80634?
  - What is the median home value in San Carlos, CA?
- source_sentence: The four new principals hired by Superintendent of Schools Ken
    Kenworthy for the Okeechobee school system are Joseph Stanley at Central Elementary,
    Jody Hays at Yearling Middle School, Tuuli Robinson at North Elementary, and Dr.
    Thelma Jackson at Seminole Elementary School.
  sentences:
  - Who won the gold medal in the men's 1,500m final at the speed skating World Cup?
  - What is the purpose of the 1,2,3 bowling activity for toddlers?
  - Who are the four new principals hired by Superintendent of Schools Ken Kenworthy
    for the Okeechobee school system?
- source_sentence: Twitter Audit is used to scan your followers and find out what
    percentage of them are real people.
  sentences:
  - What is the main product discussed in the context of fair trade?
  - What is the software mentioned in the context suitable for?
  - What is the purpose of the Twitter Audit tool?
- source_sentence: Michael Czysz made the 2011 E1pc lighter and more powerful than
    the 2010 version, and also improved the software controlling the bike’s D1g1tal
    powertrain.
  sentences:
  - What changes did Michael Czysz make to the 2011 E1pc compared to the 2010 version?
  - What is the author's suggestion for leaving a legacy for future generations?
  - What is the most affordable and reliable option to fix a MacBook according to
    the technician?
- source_sentence: HTC called the Samsung Galaxy S4 “mainstream”.
  sentences:
  - What is the essential aspect of the vocation to marriage according to Benedict
    XVI's message on the 40th Anniversary of Humanae Vitae?
  - What did HTC announce about the Samsung Galaxy S4?
  - What was Allan Cox's First Class Delivery launched on for his Level 1 certification
    flight?
model-index:
- name: BGE base Financial Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.9675
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9791666666666666
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9829166666666667
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.98875
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9675
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3263888888888889
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1965833333333333
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09887499999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9675
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9791666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9829166666666667
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.98875
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9776735843960416
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9741727843915341
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.974471752833939
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.9641666666666666
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9775
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9816666666666667
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.98875
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9641666666666666
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3258333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1963333333333333
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09887499999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9641666666666666
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9775
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9816666666666667
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.98875
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9758504869144781
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9717977843915344
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9720465527215371
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.9620833333333333
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9741666666666666
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9804166666666667
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.98625
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9620833333333333
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.32472222222222225
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1960833333333333
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09862499999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9620833333333333
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9741666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9804166666666667
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.98625
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9737941784937224
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9698406084656085
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9702070899963996
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.9554166666666667
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.97
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9766666666666667
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.98375
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9554166666666667
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3233333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1953333333333333
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09837499999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9554166666666667
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.97
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9766666666666667
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.98375
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.969307497603498
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9647410714285715
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9652034022263717
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.9391666666666667
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9616666666666667
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9666666666666667
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9758333333333333
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9391666666666667
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3205555555555556
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1933333333333333
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09758333333333333
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9391666666666667
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9616666666666667
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9666666666666667
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9758333333333333
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9577277779716886
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9519417989417989
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9525399354798056
      name: Cosine Map@100
---

# BGE base Financial Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### Model Sources

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

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

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

# Download from the 🤗 Hub
model = SentenceTransformer("juanpablomesa/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'HTC called the Samsung Galaxy S4 “mainstream”.',
    'What did HTC announce about the Samsung Galaxy S4?',
    "What is the essential aspect of the vocation to marriage according to Benedict XVI's message on the 40th Anniversary of Humanae Vitae?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
-->

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

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

## Evaluation

### Metrics

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

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9675     |
| cosine_accuracy@3   | 0.9792     |
| cosine_accuracy@5   | 0.9829     |
| cosine_accuracy@10  | 0.9888     |
| cosine_precision@1  | 0.9675     |
| cosine_precision@3  | 0.3264     |
| cosine_precision@5  | 0.1966     |
| cosine_precision@10 | 0.0989     |
| cosine_recall@1     | 0.9675     |
| cosine_recall@3     | 0.9792     |
| cosine_recall@5     | 0.9829     |
| cosine_recall@10    | 0.9888     |
| cosine_ndcg@10      | 0.9777     |
| cosine_mrr@10       | 0.9742     |
| **cosine_map@100**  | **0.9745** |

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

| Metric              | Value     |
|:--------------------|:----------|
| cosine_accuracy@1   | 0.9642    |
| cosine_accuracy@3   | 0.9775    |
| cosine_accuracy@5   | 0.9817    |
| cosine_accuracy@10  | 0.9888    |
| cosine_precision@1  | 0.9642    |
| cosine_precision@3  | 0.3258    |
| cosine_precision@5  | 0.1963    |
| cosine_precision@10 | 0.0989    |
| cosine_recall@1     | 0.9642    |
| cosine_recall@3     | 0.9775    |
| cosine_recall@5     | 0.9817    |
| cosine_recall@10    | 0.9888    |
| cosine_ndcg@10      | 0.9759    |
| cosine_mrr@10       | 0.9718    |
| **cosine_map@100**  | **0.972** |

#### 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   | 0.9621     |
| cosine_accuracy@3   | 0.9742     |
| cosine_accuracy@5   | 0.9804     |
| cosine_accuracy@10  | 0.9862     |
| cosine_precision@1  | 0.9621     |
| cosine_precision@3  | 0.3247     |
| cosine_precision@5  | 0.1961     |
| cosine_precision@10 | 0.0986     |
| cosine_recall@1     | 0.9621     |
| cosine_recall@3     | 0.9742     |
| cosine_recall@5     | 0.9804     |
| cosine_recall@10    | 0.9862     |
| cosine_ndcg@10      | 0.9738     |
| cosine_mrr@10       | 0.9698     |
| **cosine_map@100**  | **0.9702** |

#### 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   | 0.9554     |
| cosine_accuracy@3   | 0.97       |
| cosine_accuracy@5   | 0.9767     |
| cosine_accuracy@10  | 0.9838     |
| cosine_precision@1  | 0.9554     |
| cosine_precision@3  | 0.3233     |
| cosine_precision@5  | 0.1953     |
| cosine_precision@10 | 0.0984     |
| cosine_recall@1     | 0.9554     |
| cosine_recall@3     | 0.97       |
| cosine_recall@5     | 0.9767     |
| cosine_recall@10    | 0.9838     |
| cosine_ndcg@10      | 0.9693     |
| cosine_mrr@10       | 0.9647     |
| **cosine_map@100**  | **0.9652** |

#### 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   | 0.9392     |
| cosine_accuracy@3   | 0.9617     |
| cosine_accuracy@5   | 0.9667     |
| cosine_accuracy@10  | 0.9758     |
| cosine_precision@1  | 0.9392     |
| cosine_precision@3  | 0.3206     |
| cosine_precision@5  | 0.1933     |
| cosine_precision@10 | 0.0976     |
| cosine_recall@1     | 0.9392     |
| cosine_recall@3     | 0.9617     |
| cosine_recall@5     | 0.9667     |
| cosine_recall@10    | 0.9758     |
| cosine_ndcg@10      | 0.9577     |
| cosine_mrr@10       | 0.9519     |
| **cosine_map@100**  | **0.9525** |

<!--
## 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.*
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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 9,600 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: 3 tokens</li><li>mean: 50.19 tokens</li><li>max: 435 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.66 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                            | anchor                                                                                               |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|
  | <code>The Berry Export Summary 2028 is a dedicated export plan for the Australian strawberry, raspberry, and blackberry industries. It maps the sectors’ current position, where they want to be, high-opportunity markets, and next steps. The purpose of this plan is to grow their global presence over the next 10 years.</code>                                                                                                | <code>What is the Berry Export Summary 2028 and what is its purpose?</code>                          |
  | <code>Benefits reported from having access to Self-supply water sources include convenience, less time spent for fetching water and access to more and better quality water. In some areas, Self-supply sources offer important added values such as water for productive use, income generation, family safety and improved food security.</code>                                                                                  | <code>What are some of the benefits reported from having access to Self-supply water sources?</code> |
  | <code>The unique features of the Coolands for Twitter app include Real-Time updates without the need for a refresh button, Avatar Indicator which shows small avatars on the title bar for new messages, Direct Link for intuitive and convenient link opening, Smart Bookmark to easily return to previous reading position, and User Level Notification which allows customized notification settings for different users.</code> | <code>What are the unique features of the Coolands for Twitter app?</code>                           |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "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`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `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`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: 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_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:--------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.5333   | 10     | 0.6065        | -                      | -                      | -                      | -                     | -                      |
| 0.96     | 18     | -             | 0.9583                 | 0.9674                 | 0.9695                 | 0.9372                | 0.9708                 |
| 1.0667   | 20     | 0.3313        | -                      | -                      | -                      | -                     | -                      |
| 1.6      | 30     | 0.144         | -                      | -                      | -                      | -                     | -                      |
| 1.9733   | 37     | -             | 0.9630                 | 0.9699                 | 0.9716                 | 0.9488                | 0.9745                 |
| 2.1333   | 40     | 0.1317        | -                      | -                      | -                      | -                     | -                      |
| 2.6667   | 50     | 0.0749        | -                      | -                      | -                      | -                     | -                      |
| 2.9867   | 56     | -             | 0.9650                 | 0.9701                 | 0.9721                 | 0.9522                | 0.9747                 |
| 3.2      | 60     | 0.088         | -                      | -                      | -                      | -                     | -                      |
| 3.7333   | 70     | 0.0598        | -                      | -                      | -                      | -                     | -                      |
| **3.84** | **72** | **-**         | **0.9652**             | **0.9702**             | **0.972**              | **0.9525**            | **0.9745**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.5
- 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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