README.md

---
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 8 in IBM's 2023 Annual Report to Stockholders details the
    Financial Statements and Supplementary Data, which are included on pages 44 through
    121.
  sentences:
  - What was the amount gained from the disposal of assets in 2022?
  - What section of IBM's Annual Report for 2023 contains the Financial Statements
    and Supplementary Data?
  - What were the cash outflows for capital expenditures in 2023 and 2022 respectively?
- source_sentence: For the fiscal year ended March 31, 2023, Electronic Arts reported
    a gross margin of 75.9 percent, an increase of 2.5 percentage points from the
    previous year.
  sentences:
  - How did investment banking revenues at Goldman Sachs change in 2023 compared to
    2022, and what factors contributed to this change?
  - What was the gross margin percentage for Electronic Arts in the fiscal year ending
    March 31, 2023?
  - What were the risk-free interest rates for the fiscal years 2021, 2022, and 2023?
- source_sentence: Cash, cash equivalents, and restricted cash at the beginning of
    the period totaled $7,013 for a company.
  sentences:
  - What was the amount of cash, cash equivalents, and restricted cash at the beginning
    of the period for the company?
  - What is the impact of the new $1.25 price point on Dollar Tree’s sales units and
    profitability?
  - What was the total amount attributed to Goodwill in the acquisition of Nuance
    Communications, Inc. as reported by the company?
- source_sentence: generate our mall revenue primarily from leases with tenants through
    base minimum rents, overage rents and reimbursements for common area maintenance
    (CAM) and other expenditures.
  sentences:
  - How does Visa facilitate financial inclusion with their prepaid cards?
  - What are the main objectives of the economic sanctions imposed by the United States
    and other international bodies?
  - What revenue sources does Shoppes at Venetian primarily rely on from its tenants?
- source_sentence: For the fiscal year ended August 26, 2023, we reported net sales
    of $17.5 billion compared with $16.3 billion for the year ended August 27, 2022,
    a 7.4% increase from fiscal 2022. This growth was driven primarily by a domestic
    same store sales increase of 3.4% and net sales of $327.8 million from new domestic
    and international stores.
  sentences:
  - What drove the 7.4% increase in AutoZone's net sales for fiscal 2023 compared
    to fiscal 2022?
  - What percentage of HP's external U.S. hires in fiscal year 2023 were racially
    or ethnically diverse?
  - How much did GameStop Corp's valuation allowances increase during fiscal 2022?
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: 0.6985714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8271428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8628571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8985714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6985714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2757142857142857
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17257142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08985714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6985714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8271428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8628571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8985714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8023663256793517
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7712675736961451
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7758522351159084
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.69
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8271428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.86
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9028571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.69
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2757142857142857
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17199999999999996
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09028571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.69
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8271428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.86
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9028571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7998655910794988
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7665912698412698
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7706925401671437
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6957142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8228571428571428
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.86
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8914285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6957142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2742857142857143
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17199999999999996
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08914285714285713
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6957142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8228571428571428
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.86
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8914285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7974564108711016
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7669535147392289
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7718155211819018
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6871428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8128571428571428
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8457142857142858
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8857142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6871428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27095238095238094
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16914285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08857142857142856
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6871428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8128571428571428
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8457142857142858
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8857142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.787697533881839
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.756192743764172
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7610331995977764
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6328571428571429
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7771428571428571
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8171428571428572
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8571428571428571
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6328571428571429
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.259047619047619
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16342857142857142
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08571428571428569
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6328571428571429
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7771428571428571
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8171428571428572
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8571428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7482728321357093
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7131224489795914
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7189753431460272
      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("NickyNicky/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'For the fiscal year ended August 26, 2023, we reported net sales of $17.5 billion compared with $16.3 billion for the year ended August 27, 2022, a 7.4% increase from fiscal 2022. This growth was driven primarily by a domestic same store sales increase of 3.4% and net sales of $327.8 million from new domestic and international stores.',
    "What drove the 7.4% increase in AutoZone's net sales for fiscal 2023 compared to fiscal 2022?",
    "What percentage of HP's external U.S. hires in fiscal year 2023 were racially or ethnically diverse?",
]
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   | 0.6986     |
| cosine_accuracy@3   | 0.8271     |
| cosine_accuracy@5   | 0.8629     |
| cosine_accuracy@10  | 0.8986     |
| cosine_precision@1  | 0.6986     |
| cosine_precision@3  | 0.2757     |
| cosine_precision@5  | 0.1726     |
| cosine_precision@10 | 0.0899     |
| cosine_recall@1     | 0.6986     |
| cosine_recall@3     | 0.8271     |
| cosine_recall@5     | 0.8629     |
| cosine_recall@10    | 0.8986     |
| cosine_ndcg@10      | 0.8024     |
| cosine_mrr@10       | 0.7713     |
| **cosine_map@100**  | **0.7759** |

#### 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   | 0.69       |
| cosine_accuracy@3   | 0.8271     |
| cosine_accuracy@5   | 0.86       |
| cosine_accuracy@10  | 0.9029     |
| cosine_precision@1  | 0.69       |
| cosine_precision@3  | 0.2757     |
| cosine_precision@5  | 0.172      |
| cosine_precision@10 | 0.0903     |
| cosine_recall@1     | 0.69       |
| cosine_recall@3     | 0.8271     |
| cosine_recall@5     | 0.86       |
| cosine_recall@10    | 0.9029     |
| cosine_ndcg@10      | 0.7999     |
| cosine_mrr@10       | 0.7666     |
| **cosine_map@100**  | **0.7707** |

#### 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   | 0.6957     |
| cosine_accuracy@3   | 0.8229     |
| cosine_accuracy@5   | 0.86       |
| cosine_accuracy@10  | 0.8914     |
| cosine_precision@1  | 0.6957     |
| cosine_precision@3  | 0.2743     |
| cosine_precision@5  | 0.172      |
| cosine_precision@10 | 0.0891     |
| cosine_recall@1     | 0.6957     |
| cosine_recall@3     | 0.8229     |
| cosine_recall@5     | 0.86       |
| cosine_recall@10    | 0.8914     |
| cosine_ndcg@10      | 0.7975     |
| cosine_mrr@10       | 0.767      |
| **cosine_map@100**  | **0.7718** |

#### 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   | 0.6871    |
| cosine_accuracy@3   | 0.8129    |
| cosine_accuracy@5   | 0.8457    |
| cosine_accuracy@10  | 0.8857    |
| cosine_precision@1  | 0.6871    |
| cosine_precision@3  | 0.271     |
| cosine_precision@5  | 0.1691    |
| cosine_precision@10 | 0.0886    |
| cosine_recall@1     | 0.6871    |
| cosine_recall@3     | 0.8129    |
| cosine_recall@5     | 0.8457    |
| cosine_recall@10    | 0.8857    |
| cosine_ndcg@10      | 0.7877    |
| cosine_mrr@10       | 0.7562    |
| **cosine_map@100**  | **0.761** |

#### 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   | 0.6329    |
| cosine_accuracy@3   | 0.7771    |
| cosine_accuracy@5   | 0.8171    |
| cosine_accuracy@10  | 0.8571    |
| cosine_precision@1  | 0.6329    |
| cosine_precision@3  | 0.259     |
| cosine_precision@5  | 0.1634    |
| cosine_precision@10 | 0.0857    |
| cosine_recall@1     | 0.6329    |
| cosine_recall@3     | 0.7771    |
| cosine_recall@5     | 0.8171    |
| cosine_recall@10    | 0.8571    |
| cosine_ndcg@10      | 0.7483    |
| cosine_mrr@10       | 0.7131    |
| **cosine_map@100**  | **0.719** |

<!--
## 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: 2 tokens</li><li>mean: 46.19 tokens</li><li>max: 371 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.39 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                           | anchor                                                                                                                                   |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Cash used in financing activities in fiscal 2022 was primarily attributable to settlement of stock-based awards.</code>                                                                                                                      | <code>Why was there a net outflow of cash in financing activities in fiscal 2022?</code>                                                 |
  | <code>Certain vendors have been impacted by volatility in the supply chain financing market.</code>                                                                                                                                                | <code>How have certain vendors been impacted in the supply chain financing market?</code>                                                |
  | <code>In the consolidated financial statements for Visa, the net cash provided by operating activities amounted to 20,755 units in the most recent period, 18,849 units in the previous period, and 15,227 units in the period before that.</code> | <code>How much net cash did Visa's operating activities generate in the most recent period according to the financial statements?</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
- `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`: False
- `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   | 1.5643        | -                      | -                      | -                      | -                     | -                      |
| 0.9746 | 12   | -             | 0.7349                 | 0.7494                 | 0.7524                 | 0.6987                | 0.7569                 |
| 1.6244 | 20   | 0.6756        | -                      | -                      | -                      | -                     | -                      |
| 1.9492 | 24   | -             | 0.7555                 | 0.7659                 | 0.7683                 | 0.7190                | 0.7700                 |
| 2.4365 | 30   | 0.4561        | -                      | -                      | -                      | -                     | -                      |
| 2.9239 | 36   | -             | 0.7592                 | 0.7698                 | 0.7698                 | 0.7184                | 0.7741                 |
| 3.2487 | 40   | 0.3645        | -                      | -                      | -                      | -                     | -                      |
| 3.8985 | 48   | -             | 0.7610                 | 0.7718                 | 0.7707                 | 0.7190                | 0.7759                 |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.2.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## Glossary

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

<!--
## Model Card Authors

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

<!--
## Model Card Contact

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