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: What was Iron Mountain's physical records retention rate approximately
    15 years after entry into their facilities?
  sentences:
  - Garmin Connect and Garmin Connect Mobile are web and mobile platforms where users
    can track and analyze their fitness, activities and workouts, and wellness data.
  - More than 50% of physical records that entered Iron Mountain's facilities approximately
    15 years ago are still there today.
  - In the first quarter of 2023, the divestiture of the company’s Longwall business
    was finalized, resulting in an unfavorable impact to operating profit of $586
    million, primarily a non-cash item driven by the release of accumulated foreign
    currency translation.
- source_sentence: How much did the company's currently payable U.S. taxes amount
    to in 2023?
  sentences:
  - In 2023, the currently payable U.S. taxes amounted to $2,705 million.
  - The Company expects to realize at least $500 million of incremental run-rate cost
    savings in addition to integration synergies.
  - During fiscal year 2023, we returned $210 million through our quarterly cash dividend
    program which was initiated in November 2020.
- source_sentence: What was the percentage decline in GMS for the year ended December
    31, 2023 compared to 2022?
  sentences:
  - The Gross Merchandise Sales (GMS) decreased by 1.2% in 2023 compared to 2022.
  - If, in the future, foreign exchange or capital control restrictions were to be
    imposed and become applicable to us, such restrictions could potentially reduce
    the amounts that we would be able to receive from our Macao, Hong Kong and mainland
    China subsidiaries.
  - Net cash provided by operating activities decreased by $2.0 billion in fiscal
    2022 compared to fiscal 2021.
- source_sentence: What was the operating income for the year 2023?
  sentences:
  - Effective January 1, 2021, CSC changed the designation of its corporate headquarters
    from San Francisco, California to Westlake, Texas.
  - The operating income for the year 2023 was reported as -$74.3 million.
  - Table 12 shows that the total risk-weighted assets under Basel 3 for credit risk
    at Bank of America amounted to $1,580 billion as of December 31, 2023.
- source_sentence: What was the total amount of tax incurred, collected, and remitted
    by AT&T in 2023?
  sentences:
  - For example, in response to regulatory developments in Europe, we announced plans
    to change the legal basis for behavioral advertising on Facebook and Instagram
    in the EU, EEA, and Switzerland from "legitimate interests" to "consent," and
    in November 2023 we began offering users in the region a "subscription for no
    ads" alternative.
  - Professional services expenses decreased $8 million in 2023 from 2022 primarily
    due to lower consulting expenses related to bringing certain mortgage technology-related
    costs in-house, partially offset by higher legal expenses primarily related to
    the Black Knight acquisition.
  - Total taxes incurred, collected and remitted by AT&T during 2023 were $16,877.
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: 0.6771428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8328571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8614285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9085714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6771428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2776190476190476
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17228571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09085714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6771428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8328571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8614285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9085714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7950953946105658
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7584574829931973
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7618150097795325
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6785714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8257142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8642857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9014285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6785714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2752380952380952
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17285714285714282
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09014285714285714
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6785714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8257142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8642857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9014285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7927053640201507
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7574620181405893
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7614007843308703
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.68
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.81
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8528571428571429
      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: 0.27
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17057142857142854
      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: 0.81
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8528571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8971428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7889658321825918
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7541865079365075
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7582635867273656
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6614285714285715
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8385714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8914285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6614285714285715
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16771428571428568
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08914285714285713
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6614285714285715
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8385714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8914285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7751876221972102
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7381241496598633
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7423110490736153
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6257142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.78
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8214285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8728571428571429
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6257142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16428571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08728571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6257142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.78
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8214285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8728571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.750742644383485
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7114563492063489
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7163043069454876
      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("Chuangmail/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'What was the total amount of tax incurred, collected, and remitted by AT&T in 2023?',
    'Total taxes incurred, collected and remitted by AT&T during 2023 were $16,877.',
    'Professional services expenses decreased $8 million in 2023 from 2022 primarily due to lower consulting expenses related to bringing certain mortgage technology-related costs in-house, partially offset by higher legal expenses primarily related to the Black Knight acquisition.',
]
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]
```

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</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

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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   | 0.6771     |
| cosine_accuracy@3   | 0.8329     |
| cosine_accuracy@5   | 0.8614     |
| cosine_accuracy@10  | 0.9086     |
| cosine_precision@1  | 0.6771     |
| cosine_precision@3  | 0.2776     |
| cosine_precision@5  | 0.1723     |
| cosine_precision@10 | 0.0909     |
| cosine_recall@1     | 0.6771     |
| cosine_recall@3     | 0.8329     |
| cosine_recall@5     | 0.8614     |
| cosine_recall@10    | 0.9086     |
| cosine_ndcg@10      | 0.7951     |
| cosine_mrr@10       | 0.7585     |
| **cosine_map@100**  | **0.7618** |

#### 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   | 0.6786     |
| cosine_accuracy@3   | 0.8257     |
| cosine_accuracy@5   | 0.8643     |
| cosine_accuracy@10  | 0.9014     |
| cosine_precision@1  | 0.6786     |
| cosine_precision@3  | 0.2752     |
| cosine_precision@5  | 0.1729     |
| cosine_precision@10 | 0.0901     |
| cosine_recall@1     | 0.6786     |
| cosine_recall@3     | 0.8257     |
| cosine_recall@5     | 0.8643     |
| cosine_recall@10    | 0.9014     |
| cosine_ndcg@10      | 0.7927     |
| cosine_mrr@10       | 0.7575     |
| **cosine_map@100**  | **0.7614** |

#### 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   | 0.68       |
| cosine_accuracy@3   | 0.81       |
| cosine_accuracy@5   | 0.8529     |
| cosine_accuracy@10  | 0.8971     |
| cosine_precision@1  | 0.68       |
| cosine_precision@3  | 0.27       |
| cosine_precision@5  | 0.1706     |
| cosine_precision@10 | 0.0897     |
| cosine_recall@1     | 0.68       |
| cosine_recall@3     | 0.81       |
| cosine_recall@5     | 0.8529     |
| cosine_recall@10    | 0.8971     |
| cosine_ndcg@10      | 0.789      |
| cosine_mrr@10       | 0.7542     |
| **cosine_map@100**  | **0.7583** |

#### 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   | 0.6614     |
| cosine_accuracy@3   | 0.8        |
| cosine_accuracy@5   | 0.8386     |
| cosine_accuracy@10  | 0.8914     |
| cosine_precision@1  | 0.6614     |
| cosine_precision@3  | 0.2667     |
| cosine_precision@5  | 0.1677     |
| cosine_precision@10 | 0.0891     |
| cosine_recall@1     | 0.6614     |
| cosine_recall@3     | 0.8        |
| cosine_recall@5     | 0.8386     |
| cosine_recall@10    | 0.8914     |
| cosine_ndcg@10      | 0.7752     |
| cosine_mrr@10       | 0.7381     |
| **cosine_map@100**  | **0.7423** |

#### 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   | 0.6257     |
| cosine_accuracy@3   | 0.78       |
| cosine_accuracy@5   | 0.8214     |
| cosine_accuracy@10  | 0.8729     |
| cosine_precision@1  | 0.6257     |
| cosine_precision@3  | 0.26       |
| cosine_precision@5  | 0.1643     |
| cosine_precision@10 | 0.0873     |
| cosine_recall@1     | 0.6257     |
| cosine_recall@3     | 0.78       |
| cosine_recall@5     | 0.8214     |
| cosine_recall@10    | 0.8729     |
| cosine_ndcg@10      | 0.7507     |
| cosine_mrr@10       | 0.7115     |
| **cosine_map@100**  | **0.7163** |

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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>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 2 tokens</li><li>mean: 20.39 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 46.37 tokens</li><li>max: 326 tokens</li></ul> |
* Samples:
  | anchor                                                                                             | positive                                                                                                                                                                                                                                                                                             |
  |:---------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What are the key factors HP considers when making adjustments to inventory valuation?</code> | <code>HP makes adjustments to inventory valuation based on considerations of changes in demand, technological changes, supply constraints, product life cycle, component cost trends, product pricing, and quality issues.</code>                                                                    |
  | <code>What types of products does AbbVie's portfolio include?</code>                               | <code>AbbVie is a global, diversified research-based biopharmaceutical company with a comprehensive product portfolio that has leadership positions across immunology, oncology, aesthetics, neuroscience, and eye care.</code>                                                                      |
  | <code>What does IBM’s 2023 Annual Report to Stockholders include?</code>                           | <code>IBM's 2023 Annual Report to Stockholders includes their financial statements and supplementary data, which span from pages 44 to 121 and are incorporated by reference in the Form 10-K. Additionally, the financial statement schedule can be found on page S-1 of the same Form 10-K.</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     | 1.6191        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7267                 | 0.7355                 | 0.7447                 | 0.6939                | 0.7453                 |
| 1.6244     | 20     | 0.6415        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7358                 | 0.7509                 | 0.7548                 | 0.7075                | 0.7554                 |
| 2.4365     | 30     | 0.4638        | -                      | -                      | -                      | -                     | -                      |
| 2.9239     | 36     | -             | 0.7398                 | 0.7573                 | 0.7607                 | 0.7124                | 0.7601                 |
| 3.2487     | 40     | 0.4083        | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **48** | **-**         | **0.7423**             | **0.7583**             | **0.7614**             | **0.7163**            | **0.7618**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.3.0
- 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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