sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:6300 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:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: ITEM 7. MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION
    AND RESULTS OF OPERATIONS The following discussion and analysis should be read
    in conjunction with the consolidated financial statements and the related notes
    included elsewhere in this Annual Report on Form 10-K. For further discussion
    of our products and services, technology and competitive strengths, refer to Item
    1- Business.
  sentences:
  - What was the total net automotive cash provided by investing activities in 2023?
  - What is the purpose of the Management's Discussion and Analysis of Financial Condition
    and Results of Operations section in the Annual Report on Form 10-K?
  - What are the components included in the management discussion and analysis of
    financial condition and results of operations?
- source_sentence: Kroger is committed to maintaining a net total debt to adjusted
    EBITDA ratio target range of 2.30 to 2.50.
  sentences:
  - What was the remaining available amount of the share repurchase authorization
    as of January 29, 2023?
  - What range does Kroger aim for its net total debt to adjusted EBITDA ratio?
  - What was the starting wage for all entry-level positions in the U.S. as of September
    2023?
- source_sentence: Google Cloud operating income of $1.7 billion for 2023.
  sentences:
  - What was the operating income for Google Cloud in 2023?
  - What types of products are offered in Garmin's Fitness segment?
  - What was the net sales of the company in fiscal 2022?
- source_sentence: The effective income tax rate for Alphabet Inc. at the end of the
    year 2023 was 13.9%.
  sentences:
  - What was the percentage change in Compute & Networking revenue from fiscal year
    2022 to 2023?
  - What factors primarily contributed to the increase in non-interest revenues across
    all revenue categories?
  - What was the effective income tax rate for Alphabet Inc. at the end of the year
    2023?
- source_sentence: State legislation increasingly requires PBMs to conduct audits
    of network pharmacies regarding claims submitted for payment. Non-compliance could
    prevent the recoupment of overpaid amounts, potentially causing financial and
    legal repercussions.
  sentences:
  - What are the potential consequences for a company if its PBMs fail to comply with
    pharmacy audit regulations?
  - What pages do the Consolidated Financial Statements and their accompanying Notes
    and reports appear on in the document?
  - What are the primary services provided by the company under the Xfinity, Comcast
    Business, and Sky brands?
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: 0.6785714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8342857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.88
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9085714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6785714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2780952380952381
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.176
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09085714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6785714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8342857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.88
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9085714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7995179593313807
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7638202947845802
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7674168947978975
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6685714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8271428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8685714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9128571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6685714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2757142857142857
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1737142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09128571428571428
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6685714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8271428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8685714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9128571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7954721927324272
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7574353741496596
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7606771546726785
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6728571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8142857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8642857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9042857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6728571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2714285714285714
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17285714285714285
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09042857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6728571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8142857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8642857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9042857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7916203877025221
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7552613378684805
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7590698804335085
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6528571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8114285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.85
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8885714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6528571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2704761904761904
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08885714285714286
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6528571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8114285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.85
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8885714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7754227314755763
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.738630385487528
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7431237490151862
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6157142857142858
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7614285714285715
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.81
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8642857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6157142857142858
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2538095238095238
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16199999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08642857142857142
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6157142857142858
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7614285714285715
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.81
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8642857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7413954849024657
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.701954648526077
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.707051130510896
      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("gauravsirola/bge-base-financial-matryoshka-v1")
# Run inference
sentences = [
    'State legislation increasingly requires PBMs to conduct audits of network pharmacies regarding claims submitted for payment. Non-compliance could prevent the recoupment of overpaid amounts, potentially causing financial and legal repercussions.',
    'What are the potential consequences for a company if its PBMs fail to comply with pharmacy audit regulations?',
    'What pages do the Consolidated Financial Statements and their accompanying Notes and reports appear on in the document?',
]
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   | 0.6786     |
| cosine_accuracy@3   | 0.8343     |
| cosine_accuracy@5   | 0.88       |
| cosine_accuracy@10  | 0.9086     |
| cosine_precision@1  | 0.6786     |
| cosine_precision@3  | 0.2781     |
| cosine_precision@5  | 0.176      |
| cosine_precision@10 | 0.0909     |
| cosine_recall@1     | 0.6786     |
| cosine_recall@3     | 0.8343     |
| cosine_recall@5     | 0.88       |
| cosine_recall@10    | 0.9086     |
| cosine_ndcg@10      | 0.7995     |
| cosine_mrr@10       | 0.7638     |
| **cosine_map@100**  | **0.7674** |

#### 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   | 0.6686     |
| cosine_accuracy@3   | 0.8271     |
| cosine_accuracy@5   | 0.8686     |
| cosine_accuracy@10  | 0.9129     |
| cosine_precision@1  | 0.6686     |
| cosine_precision@3  | 0.2757     |
| cosine_precision@5  | 0.1737     |
| cosine_precision@10 | 0.0913     |
| cosine_recall@1     | 0.6686     |
| cosine_recall@3     | 0.8271     |
| cosine_recall@5     | 0.8686     |
| cosine_recall@10    | 0.9129     |
| cosine_ndcg@10      | 0.7955     |
| cosine_mrr@10       | 0.7574     |
| **cosine_map@100**  | **0.7607** |

#### 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   | 0.6729     |
| cosine_accuracy@3   | 0.8143     |
| cosine_accuracy@5   | 0.8643     |
| cosine_accuracy@10  | 0.9043     |
| cosine_precision@1  | 0.6729     |
| cosine_precision@3  | 0.2714     |
| cosine_precision@5  | 0.1729     |
| cosine_precision@10 | 0.0904     |
| cosine_recall@1     | 0.6729     |
| cosine_recall@3     | 0.8143     |
| cosine_recall@5     | 0.8643     |
| cosine_recall@10    | 0.9043     |
| cosine_ndcg@10      | 0.7916     |
| cosine_mrr@10       | 0.7553     |
| **cosine_map@100**  | **0.7591** |

#### 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   | 0.6529     |
| cosine_accuracy@3   | 0.8114     |
| cosine_accuracy@5   | 0.85       |
| cosine_accuracy@10  | 0.8886     |
| cosine_precision@1  | 0.6529     |
| cosine_precision@3  | 0.2705     |
| cosine_precision@5  | 0.17       |
| cosine_precision@10 | 0.0889     |
| cosine_recall@1     | 0.6529     |
| cosine_recall@3     | 0.8114     |
| cosine_recall@5     | 0.85       |
| cosine_recall@10    | 0.8886     |
| cosine_ndcg@10      | 0.7754     |
| cosine_mrr@10       | 0.7386     |
| **cosine_map@100**  | **0.7431** |

#### 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   | 0.6157     |
| cosine_accuracy@3   | 0.7614     |
| cosine_accuracy@5   | 0.81       |
| cosine_accuracy@10  | 0.8643     |
| cosine_precision@1  | 0.6157     |
| cosine_precision@3  | 0.2538     |
| cosine_precision@5  | 0.162      |
| cosine_precision@10 | 0.0864     |
| cosine_recall@1     | 0.6157     |
| cosine_recall@3     | 0.7614     |
| cosine_recall@5     | 0.81       |
| cosine_recall@10    | 0.8643     |
| cosine_ndcg@10      | 0.7414     |
| cosine_mrr@10       | 0.702      |
| **cosine_map@100**  | **0.7071** |

<!--
## 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: 6,300 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: 7 tokens</li><li>mean: 44.73 tokens</li><li>max: 301 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.57 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                   | anchor                                                                                                                                |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Net loss was $396.6 million and $973.6 million during the years ended December 31, 2023, and December 31, 2022, respectively.</code>                                                 | <code>What was the net loss for the year ended December 31, 2022?</code>                                                              |
  | <code>Under the 2023 IDA agreement, the service fee on client cash deposits held at the TD Depository Institutions remains at 15 basis points, as it was in the 2019 IDA agreement.</code> | <code>How much is the service fee on client cash deposits held at the TD Depository Institutions under the 2023 IDA agreement?</code> |
  | <code>The total shareholders’ deficit is listed as $7,994.8 million in the latest financial statement.</code>                                                                              | <code>What is the total shareholder's deficit according to the latest financial statement?</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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8122     | 10     | 1.5585        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7207                 | 0.7441                 | 0.7510                 | 0.6857                | 0.7493                 |
| 1.6244     | 20     | 0.6691        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7392                 | 0.7564                 | 0.7601                 | 0.7006                | 0.7661                 |
| 2.4365     | 30     | 0.4702        | -                      | -                      | -                      | -                     | -                      |
| 2.9239     | 36     | -             | 0.7430                 | 0.7600                 | 0.7619                 | 0.7065                | 0.7685                 |
| 3.2487     | 40     | 0.407         | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **48** | **-**         | **0.7431**             | **0.7591**             | **0.7607**             | **0.7071**            | **0.7674**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.6
- 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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