README.md

---
language:
- en
license: cc-by-nc-sa-4.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
widget:
- source_sentence: What was the main reason for the decrease in U.S. dialysis treatments
    in 2023?
  sentences:
  - ' •Net earnings decreased modestly by $55 million to $14.7 billion versus year
    ago as the increase in operating income was more than fully offset by a higher
    effective tax rate. Foreign exchange impacts reduced net earnings by approximately
    $1.4 billion. '
  - The decrease in U.S. dialysis treatments in 2023 was primarily driven by fewer
    treatment days.
  - In the 2023 Annual Report for IBM, the Financial Statements and Supplementary
    Data are covered on pages 44 through 121.
- source_sentence: What credit ratings were assigned to the company by Standard &
    Poor’s and Moody’s at the end of 2022?
  sentences:
  - As of January 28, 2023, the total financial obligations listed for 2027 amounted
    to $2,210 million according to the summary table.
  - Our investment-grade credit rating at December 31, 2023 was BBB+ according to
    Standard & Poor’s Rating Services, or S&P, and Baa2 according to Moody’s Investors
    Services, Inc., or Moody’s.
  - Adjusted net earnings of $4.23 per diluted share for 2022 represented an increase
    of 14.9% compared to adjusted net earnings of $3.68 per diluted share for 2021.
- source_sentence: What does qui tam litigation refer to in the context of legal proceedings?
  sentences:
  - Qui tam litigation in legal proceedings involves litigation brought by individuals
    who are attempting to sue on behalf of the government.
  - The total fair value of awards vested during 2023 was $77,626.
  - Beginning in the first quarter of fiscal 2025, following the complete implementation
    of the one FedEx consolidation plan, FedEx will adopt a resegmented structure
    that will be aligned with how management intends to evaluate performance and allocate
    resources.
- source_sentence: What financial effect does an increase in the discount rate have
    on intangible asset valuations?
  sentences:
  - Beginning in the fourth quarter of 2023, our Family metrics no longer include
    Messenger Kids users.
  - We use comparable sales as a metric to evaluate the performance of our business.
    Refer to the Comparable Sales and Sales Per Square Foot section of this management's
    discussion and analysis of financial condition and results of operations for further
    information.
  - Changes in the discount rate, like an increase, can lead to recognizing an impairment
    of an intangible asset in spite of achieving forecasted or greater cash flows.
- source_sentence: On which pages does the Glossary of Terms and Acronyms appear in
    the financial document?
  sentences:
  - The 'Glossary of Terms and Acronyms' is included on pages 315-321 in the financial
    document.
  - Total operating expenses for the fiscal year ended January 31 were $21,962 million
    in 2023 and $18,918 million in 2022.
  - As of a recent fiscal year, approximately $12.5 billion of the $15.0 billion share
    repurchase authorization remained available.
pipeline_tag: sentence-similarity
model-index:
- name: BGE based finetuned on Domain
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.7042857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8328571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8728571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9185714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7042857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2776190476190476
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17457142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09185714285714283
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7042857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8328571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8728571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9185714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.812401187613736
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7784172335600903
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7815095527802808
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7014285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8328571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.87
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9142857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7014285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2776190476190476
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.174
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09142857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7014285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8328571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.87
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9142857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.809056064041375
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.775240362811791
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7786994072067401
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7042857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8228571428571428
      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: 0.7042857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2742857142857143
      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: 0.7042857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8228571428571428
      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: 0.80842418168086
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7750958049886617
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7786073403809471
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.68
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8185714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8514285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9057142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.68
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27285714285714285
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17028571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09057142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.68
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8185714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8514285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9057142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7928737154031139
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7568611111111109
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.760752382280591
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6685714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7914285714285715
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8257142857142857
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8771428571428571
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6685714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2638095238095238
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16514285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0877142857142857
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6685714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7914285714285715
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8257142857142857
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8771428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7719584095167248
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7385481859410428
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7436098705616472
      name: Cosine Map@100
---

# BGE based finetuned on Domain

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:** cc-by-nc-sa-4.0

### 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("tlphams/test_bge_finetuned_v0.1")
# Run inference
sentences = [
    'On which pages does the Glossary of Terms and Acronyms appear in the financial document?',
    "The 'Glossary of Terms and Acronyms' is included on pages 315-321 in the financial document.",
    'Total operating expenses for the fiscal year ended January 31 were $21,962 million in 2023 and $18,918 million in 2022.',
]
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.*
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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   | 0.7043     |
| cosine_accuracy@3   | 0.8329     |
| cosine_accuracy@5   | 0.8729     |
| cosine_accuracy@10  | 0.9186     |
| cosine_precision@1  | 0.7043     |
| cosine_precision@3  | 0.2776     |
| cosine_precision@5  | 0.1746     |
| cosine_precision@10 | 0.0919     |
| cosine_recall@1     | 0.7043     |
| cosine_recall@3     | 0.8329     |
| cosine_recall@5     | 0.8729     |
| cosine_recall@10    | 0.9186     |
| cosine_ndcg@10      | 0.8124     |
| cosine_mrr@10       | 0.7784     |
| **cosine_map@100**  | **0.7815** |

#### 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   | 0.7014     |
| cosine_accuracy@3   | 0.8329     |
| cosine_accuracy@5   | 0.87       |
| cosine_accuracy@10  | 0.9143     |
| cosine_precision@1  | 0.7014     |
| cosine_precision@3  | 0.2776     |
| cosine_precision@5  | 0.174      |
| cosine_precision@10 | 0.0914     |
| cosine_recall@1     | 0.7014     |
| cosine_recall@3     | 0.8329     |
| cosine_recall@5     | 0.87       |
| cosine_recall@10    | 0.9143     |
| cosine_ndcg@10      | 0.8091     |
| cosine_mrr@10       | 0.7752     |
| **cosine_map@100**  | **0.7787** |

#### 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   | 0.7043     |
| cosine_accuracy@3   | 0.8229     |
| cosine_accuracy@5   | 0.87       |
| cosine_accuracy@10  | 0.9129     |
| cosine_precision@1  | 0.7043     |
| cosine_precision@3  | 0.2743     |
| cosine_precision@5  | 0.174      |
| cosine_precision@10 | 0.0913     |
| cosine_recall@1     | 0.7043     |
| cosine_recall@3     | 0.8229     |
| cosine_recall@5     | 0.87       |
| cosine_recall@10    | 0.9129     |
| cosine_ndcg@10      | 0.8084     |
| cosine_mrr@10       | 0.7751     |
| **cosine_map@100**  | **0.7786** |

#### 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   | 0.68       |
| cosine_accuracy@3   | 0.8186     |
| cosine_accuracy@5   | 0.8514     |
| cosine_accuracy@10  | 0.9057     |
| cosine_precision@1  | 0.68       |
| cosine_precision@3  | 0.2729     |
| cosine_precision@5  | 0.1703     |
| cosine_precision@10 | 0.0906     |
| cosine_recall@1     | 0.68       |
| cosine_recall@3     | 0.8186     |
| cosine_recall@5     | 0.8514     |
| cosine_recall@10    | 0.9057     |
| cosine_ndcg@10      | 0.7929     |
| cosine_mrr@10       | 0.7569     |
| **cosine_map@100**  | **0.7608** |

#### 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   | 0.6686     |
| cosine_accuracy@3   | 0.7914     |
| cosine_accuracy@5   | 0.8257     |
| cosine_accuracy@10  | 0.8771     |
| cosine_precision@1  | 0.6686     |
| cosine_precision@3  | 0.2638     |
| cosine_precision@5  | 0.1651     |
| cosine_precision@10 | 0.0877     |
| cosine_recall@1     | 0.6686     |
| cosine_recall@3     | 0.7914     |
| cosine_recall@5     | 0.8257     |
| cosine_recall@10    | 0.8771     |
| cosine_ndcg@10      | 0.772      |
| cosine_mrr@10       | 0.7385     |
| **cosine_map@100**  | **0.7436** |

<!--
## 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

#### Unnamed Dataset


* 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: 8 tokens</li><li>mean: 20.71 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 44.93 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                      | positive                                                                                                                                                                                                                               |
  |:--------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What were the changes in cash flow from investing activities for the fiscal years 2023 and 2022, and what drove these changes?</code> | <code>The cash flow from investing activities experienced significant changes between 2023 and 2022, influenced by the net changes in short-term investments, which shifted from an outflow to an inflow.</code>                       |
  | <code>How much did the stock-based compensation expenses change in 2023 compared to 2022?</code>                                            | <code>Stock-based compensation expenses decreased by $88.9 million, or 16%, for the year ended December 31, 2023 compared to 2022.</code>                                                                                              |
  | <code>How does Credit Karma support its financial services?</code>                                                                          | <code>To provide these services to its members, Credit Karma works with a variety of partners, including credit bureaus, banks, credit card issuers, insurance carriers, and other financial institutions and lending partners.</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   | 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 |
|:----------:|:------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.9746     | 12     | 0.7475                 | 0.7654                 | 0.7693                 | 0.7059                | 0.7741                 |
| 1.9492     | 24     | 0.7548                 | 0.7733                 | 0.7770                 | 0.7325                | 0.7761                 |
| 2.9239     | 36     | 0.7599                 | 0.7784                 | 0.7782                 | 0.7429                | 0.7818                 |
| **3.8985** | **48** | **0.7608**             | **0.7786**             | **0.7787**             | **0.7436**            | **0.7815**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- 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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