---
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: Total company-operated stores | 711 | | 655
  sentences:
  - What type of financial documents are included in Part IV, Item 15(a)(1) of the
    Annual Report on Form 10-K?
  - What is the total number of company-operated stores as of January 28, 2024?
  - When does the 364-day facility entered into in August 2023 expire, and what is
    its total amount?
- source_sentence: GM empowers employees to 'Speak Up for Safety' through the Employee
    Safety Concern Process which makes it easier for employees to report potential
    safety issues or suggest improvements without fear of retaliation and ensures
    their safety every day.
  sentences:
  - What item number is associated with financial statements and supplementary data
    in documents?
  - How does GM promote safety and well-being among its employees?
  - What are the main features included in the Skills for Jobs initiative launched
    by Microsoft?
- source_sentence: Under the 2020 Plan, the exercise price of options granted is generally
    at least equal to the fair market value of the Company’s Class A common stock
    on the date of grant.
  sentences:
  - How is the exercise price for incentive stock options determined under Palantir
    Technologies Inc.’s 2020 Equity Incentive Plan?
  - What were the dividend amounts declared by AT&T for its preferred and common shares
    in December 2022 and December 2023?
  - What does Item 8 in a document usually represent?
- source_sentence: On December 22, 2022, the parties entered into a settlement agreement
    to resolve the lawsuit, which provides for a payment of $725 million by us. The
    settlement was approved by the court on October 10, 2023, and the payment was
    made in November 2023.
  sentences:
  - What is the purpose of GM's collaboration efforts at their Global Technical Center
    in Warren, Michigan?
  - How does the acquisition method affect the financial statements after a business
    acquisition?
  - What was the outcome of the 2019 consumer class action regarding the company's
    user data practices?
- source_sentence: Item 8, titled 'Financial Statements and Supplementary Data,' is
    followed by an index to these sections.
  sentences:
  - What section follows Item 8 in the document?
  - What is the total assets and shareholders' equity of Chubb Limited as of December
    31, 2023?
  - How does AT&T emphasize diversity in its hiring practices?
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: 0.7385714285714285
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8642857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8942857142857142
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9342857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7385714285714285
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28809523809523807
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17885714285714285
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09342857142857142
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7385714285714285
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8642857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8942857142857142
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9342857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8387370920568787
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8078395691609976
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8102903092098301
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7414285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8557142857142858
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8942857142857142
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9328571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7414285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2852380952380953
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17885714285714285
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09328571428571426
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7414285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8557142857142858
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8942857142857142
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9328571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8380676321786823
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8075895691609978
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8101143502932845
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7357142857142858
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.85
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8814285714285715
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.92
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7357142857142858
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2833333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17628571428571424
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09199999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7357142857142858
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.85
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8814285714285715
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.92
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8286016704428653
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7992942176870748
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8028214002001232
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.7142857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.84
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.87
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9128571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7142857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.174
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09128571428571428
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7142857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.84
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.87
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9128571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8153680997284491
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7840521541950115
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7875962124214356
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6771428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8085714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8371428571428572
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8857142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6771428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26952380952380955
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1674285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08857142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6771428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8085714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8371428571428572
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8857142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7840147713456539
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7513815192743762
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.755682487136274
      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("tessimago/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "Item 8, titled 'Financial Statements and Supplementary Data,' is followed by an index to these sections.",
    'What section follows Item 8 in the document?',
    "What is the total assets and shareholders' equity of Chubb Limited as of December 31, 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>
-->

<!--
### 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   | 0.7386     |
| cosine_accuracy@3   | 0.8643     |
| cosine_accuracy@5   | 0.8943     |
| cosine_accuracy@10  | 0.9343     |
| cosine_precision@1  | 0.7386     |
| cosine_precision@3  | 0.2881     |
| cosine_precision@5  | 0.1789     |
| cosine_precision@10 | 0.0934     |
| cosine_recall@1     | 0.7386     |
| cosine_recall@3     | 0.8643     |
| cosine_recall@5     | 0.8943     |
| cosine_recall@10    | 0.9343     |
| cosine_ndcg@10      | 0.8387     |
| cosine_mrr@10       | 0.8078     |
| **cosine_map@100**  | **0.8103** |

#### 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   | 0.7414     |
| cosine_accuracy@3   | 0.8557     |
| cosine_accuracy@5   | 0.8943     |
| cosine_accuracy@10  | 0.9329     |
| cosine_precision@1  | 0.7414     |
| cosine_precision@3  | 0.2852     |
| cosine_precision@5  | 0.1789     |
| cosine_precision@10 | 0.0933     |
| cosine_recall@1     | 0.7414     |
| cosine_recall@3     | 0.8557     |
| cosine_recall@5     | 0.8943     |
| cosine_recall@10    | 0.9329     |
| cosine_ndcg@10      | 0.8381     |
| cosine_mrr@10       | 0.8076     |
| **cosine_map@100**  | **0.8101** |

#### 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   | 0.7357     |
| cosine_accuracy@3   | 0.85       |
| cosine_accuracy@5   | 0.8814     |
| cosine_accuracy@10  | 0.92       |
| cosine_precision@1  | 0.7357     |
| cosine_precision@3  | 0.2833     |
| cosine_precision@5  | 0.1763     |
| cosine_precision@10 | 0.092      |
| cosine_recall@1     | 0.7357     |
| cosine_recall@3     | 0.85       |
| cosine_recall@5     | 0.8814     |
| cosine_recall@10    | 0.92       |
| cosine_ndcg@10      | 0.8286     |
| cosine_mrr@10       | 0.7993     |
| **cosine_map@100**  | **0.8028** |

#### 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   | 0.7143     |
| cosine_accuracy@3   | 0.84       |
| cosine_accuracy@5   | 0.87       |
| cosine_accuracy@10  | 0.9129     |
| cosine_precision@1  | 0.7143     |
| cosine_precision@3  | 0.28       |
| cosine_precision@5  | 0.174      |
| cosine_precision@10 | 0.0913     |
| cosine_recall@1     | 0.7143     |
| cosine_recall@3     | 0.84       |
| cosine_recall@5     | 0.87       |
| cosine_recall@10    | 0.9129     |
| cosine_ndcg@10      | 0.8154     |
| cosine_mrr@10       | 0.7841     |
| **cosine_map@100**  | **0.7876** |

#### 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   | 0.6771     |
| cosine_accuracy@3   | 0.8086     |
| cosine_accuracy@5   | 0.8371     |
| cosine_accuracy@10  | 0.8857     |
| cosine_precision@1  | 0.6771     |
| cosine_precision@3  | 0.2695     |
| cosine_precision@5  | 0.1674     |
| cosine_precision@10 | 0.0886     |
| cosine_recall@1     | 0.6771     |
| cosine_recall@3     | 0.8086     |
| cosine_recall@5     | 0.8371     |
| cosine_recall@10    | 0.8857     |
| cosine_ndcg@10      | 0.784      |
| cosine_mrr@10       | 0.7514     |
| **cosine_map@100**  | **0.7557** |

<!--
## 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.*
-->

## 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: 6 tokens</li><li>mean: 46.25 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.69 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
  | positive                                                                                                              | anchor                                                                                                |
  |:----------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|
  | <code>As of January 28, 2024, we held cash and cash equivalents of $2.2 billion.</code>                               | <code>What was the total cash and cash equivalents held by the company as of January 28, 2024?</code> |
  | <code>Net cash used in financing activities amounted to $1,600 million in fiscal year 2023.</code>                    | <code>What was the total net cash used in financing activities in fiscal year 2023?</code>            |
  | <code>Item 8, titled 'Financial Statements and Supplementary Data,' is followed by an index to these sections.</code> | <code>What section follows Item 8 in the document?</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     | 1.5849        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7610                 | 0.7799                 | 0.7878                 | 0.7254                | 0.7922                 |
| 1.6244     | 20     | 0.6368        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7823                 | 0.7974                 | 0.8047                 | 0.7515                | 0.8046                 |
| 2.4365     | 30     | 0.4976        | -                      | -                      | -                      | -                     | -                      |
| **2.9239** | **36** | **-**         | **0.7876**             | **0.803**              | **0.8096**             | **0.754**             | **0.8081**             |
| 3.2487     | 40     | 0.3845        | -                      | -                      | -                      | -                     | -                      |
| 3.8985     | 48     | -             | 0.7876                 | 0.8028                 | 0.8101                 | 0.7557                | 0.8103                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- 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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