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
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: 'The platform offers a number of free services to its members:
    access to their credit scores and reports, credit and identity monitoring, credit
    report dispute, tools to help understand net worth and make financial progress,
    and personalized recommendations of credit card, loan, and insurance products.
    Credit Karma Money offers members online savings and checking accounts through
    an FDIC member bank partner. Credit Karma Money also provides tools to help members
    improve their credit scores.'
  sentences:
  - What is the mechanism of action for Veklury?
  - What services does Credit Karma offer to its members?
  - What was the annual amortization expense forecast for acquisition-related intangible
    assets in 2025, according to a specified financial projection?
- source_sentence: Vaccine related exit costs of $0.8 billion were reported in the
    2023 annual report.
  sentences:
  - What factors primarily drove the decrease in Veklury's sales in 2023?
  - What were the vaccine related exit costs reported by Johnson & Johnson in their
    2023 annual report?
  - What was the percentage increase in interest income from 2022 to 2023?
- source_sentence: Broadband revenues increased in 2023 by 8.1% driven by an increase
    in fiber customers and higher average revenue per user, partially offset by declines
    in copper-based broadband services.
  sentences:
  - What was the percent change in broadband revenues for AT&T in 2023 compared to
    2022?
  - What factors primarily drove the increase in net cash provided by operating activities
    for fiscal 2023?
  - How much interest does Chevron hold in the production sharing contract for deepwater
    Block 14?
- source_sentence: SEC regulations require the company to disclose certain information
    about proceedings arising under federal, state or local environmental regulations
    if they reasonably believe that such proceedings may result in monetary sanctions
    exceeding $1 million.
  sentences:
  - What does the term 'Acquired brands' refer to and how does it affect the reported
    volumes?
  - How many new medicine candidates are currently in clinical development or under
    regulatory review?
  - Under what conditions are the Company required to disclose certain proceedings
    according to SEC regulations?
- source_sentence: 2023 highlights include net revenues of $5,003.3 million which
    decreased 15% from $5,856.7 million in 2022.
  sentences:
  - How did Hasbro's net revenues in 2023 compare to the previous year?
  - How much cash did continuing operating activities provide in 2023?
  - Which pages of IBM’s 2023 Annual Report provide information on Financial Statements
    and Supplementary Data?
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: 0.68
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.81
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8514285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8942857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.68
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17028571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08942857142857143
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.68
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.81
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8514285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8942857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7882073443841624
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7541315192743764
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7584597649275473
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.68
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8028571428571428
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8457142857142858
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8971428571428571
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.68
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2676190476190476
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16914285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0897142857142857
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.68
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8028571428571428
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8457142857142858
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8971428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7870684908640463
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7519659863945578
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7559459500178702
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6714285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7985714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8457142857142858
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8842857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6714285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2661904761904762
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16914285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08842857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6714285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7985714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8457142857142858
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8842857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7799432706618373
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7462352607709751
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7505911400077954
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.66
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7914285714285715
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8285714285714286
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8814285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.66
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2638095238095238
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1657142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08814285714285712
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.66
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7914285714285715
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8285714285714286
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8814285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7707461487192945
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7354421768707481
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7395774801009367
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6271428571428571
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7542857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8014285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.86
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6271428571428571
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25142857142857145
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16028571428571428
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08599999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6271428571428571
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7542857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8014285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.86
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7403886246637359
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7025532879818592
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7068862427781479
      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) on the json dataset. 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:**
    - json
- **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("amichelini/bge-base-financial-matryoshka")
# Run inference
sentences = [
    '2023 highlights include net revenues of $5,003.3 million which decreased 15% from $5,856.7 million in 2022.',
    "How did Hasbro's net revenues in 2023 compare to the previous year?",
    'How much cash did continuing operating activities provide in 2023?',
]
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>
-->

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
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### Out-of-Scope Use

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

### Metrics

#### Information Retrieval
* Dataset: `dim_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   | 0.68       |
| cosine_accuracy@3   | 0.81       |
| cosine_accuracy@5   | 0.8514     |
| cosine_accuracy@10  | 0.8943     |
| cosine_precision@1  | 0.68       |
| cosine_precision@3  | 0.27       |
| cosine_precision@5  | 0.1703     |
| cosine_precision@10 | 0.0894     |
| cosine_recall@1     | 0.68       |
| cosine_recall@3     | 0.81       |
| cosine_recall@5     | 0.8514     |
| cosine_recall@10    | 0.8943     |
| cosine_ndcg@10      | 0.7882     |
| cosine_mrr@10       | 0.7541     |
| **cosine_map@100**  | **0.7585** |

#### 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   | 0.68       |
| cosine_accuracy@3   | 0.8029     |
| cosine_accuracy@5   | 0.8457     |
| cosine_accuracy@10  | 0.8971     |
| cosine_precision@1  | 0.68       |
| cosine_precision@3  | 0.2676     |
| cosine_precision@5  | 0.1691     |
| cosine_precision@10 | 0.0897     |
| cosine_recall@1     | 0.68       |
| cosine_recall@3     | 0.8029     |
| cosine_recall@5     | 0.8457     |
| cosine_recall@10    | 0.8971     |
| cosine_ndcg@10      | 0.7871     |
| cosine_mrr@10       | 0.752      |
| **cosine_map@100**  | **0.7559** |

#### 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   | 0.6714     |
| cosine_accuracy@3   | 0.7986     |
| cosine_accuracy@5   | 0.8457     |
| cosine_accuracy@10  | 0.8843     |
| cosine_precision@1  | 0.6714     |
| cosine_precision@3  | 0.2662     |
| cosine_precision@5  | 0.1691     |
| cosine_precision@10 | 0.0884     |
| cosine_recall@1     | 0.6714     |
| cosine_recall@3     | 0.7986     |
| cosine_recall@5     | 0.8457     |
| cosine_recall@10    | 0.8843     |
| cosine_ndcg@10      | 0.7799     |
| cosine_mrr@10       | 0.7462     |
| **cosine_map@100**  | **0.7506** |

#### 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   | 0.66       |
| cosine_accuracy@3   | 0.7914     |
| cosine_accuracy@5   | 0.8286     |
| cosine_accuracy@10  | 0.8814     |
| cosine_precision@1  | 0.66       |
| cosine_precision@3  | 0.2638     |
| cosine_precision@5  | 0.1657     |
| cosine_precision@10 | 0.0881     |
| cosine_recall@1     | 0.66       |
| cosine_recall@3     | 0.7914     |
| cosine_recall@5     | 0.8286     |
| cosine_recall@10    | 0.8814     |
| cosine_ndcg@10      | 0.7707     |
| cosine_mrr@10       | 0.7354     |
| **cosine_map@100**  | **0.7396** |

#### 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   | 0.6271     |
| cosine_accuracy@3   | 0.7543     |
| cosine_accuracy@5   | 0.8014     |
| cosine_accuracy@10  | 0.86       |
| cosine_precision@1  | 0.6271     |
| cosine_precision@3  | 0.2514     |
| cosine_precision@5  | 0.1603     |
| cosine_precision@10 | 0.086      |
| cosine_recall@1     | 0.6271     |
| cosine_recall@3     | 0.7543     |
| cosine_recall@5     | 0.8014     |
| cosine_recall@10    | 0.86       |
| cosine_ndcg@10      | 0.7404     |
| cosine_mrr@10       | 0.7026     |
| **cosine_map@100**  | **0.7069** |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### json

* Dataset: json
* 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: 4 tokens</li><li>mean: 46.33 tokens</li><li>max: 326 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.38 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                      | anchor                                                                                                                                               |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The data includes transaction and integration costs listed as follows for each year: $0, $0, $59, $0, $0, $0, $269, $91, $39, $269, $91, $98.</code>                                                    | <code>What were the values of transaction and integration costs for each of the years provided in the data?</code>                                   |
  | <code>In 2023, Delta Air Lines announced an increase in remuneration from their partnership with American Express to $6.8 billion, with expected growth of 10% in 2024.</code>                                | <code>What was the remuneration from Delta Air Lines' partnership with American Express in 2023, and what is the growth expectation for 2024?</code> |
  | <code>On December 1, 2023, we advanced $10.0 billion under the ASR program and received approximately 215 million shares of common stock with a value of $6.8 billion, which were immediately retired.</code> | <code>What significant financial activity occurred on December 1, 2023, under the ASR program?</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
- `torch_empty_cache_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
- `eval_on_start`: False
- `eval_use_gather_object`: 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          | 0      | -             | 0.6648                 | 0.6922                 | 0.6982                 | 0.6028                | 0.7029                 |
| 0.8122     | 10     | 1.5362        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7259                 | 0.7402                 | 0.7481                 | 0.6913                | 0.7510                 |
| 1.6244     | 20     | 0.6012        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7341                 | 0.7503                 | 0.7554                 | 0.7051                | 0.7576                 |
| 2.4365     | 30     | 0.4225        | -                      | -                      | -                      | -                     | -                      |
| 2.9239     | 36     | -             | 0.7383                 | 0.7522                 | 0.7569                 | 0.7063                | 0.7570                 |
| 3.2487     | 40     | 0.358         | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **48** | **-**         | **0.7396**             | **0.7506**             | **0.7559**             | **0.7069**            | **0.7585**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.0
- 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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