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_ndcg@100
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6201
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: ' entirety. This is a form of ownership that can only be created
    by married persons. Both spouses hold title to the whole property with the right
    of survivorship. When one spouse dies, the surviving spouse takes title to the
    property. When the second spouse dies, the property is distributed to the heirs
    according to the terms of the will. Tenants in Common. Jointly owned assets may
    also be held as tenants in common. With this form of ownership, each owner  holds
    a share of the property, which may or may not be equal. When one owner dies, his
    or her share passes immediately to that persons heirs, according to the laws in
    each state. Bank accounts, securities accounts and certificates of deposit can
    be set up as joint accounts, which may provide liquidity after your death. For
    example, you could open a joint checking account, with right of YOUR LEGACY An
    Estate-Planning Guide 13 survivorship, with one of your adult children. After
    your death, the adult child would'
  sentences:
  - What determines the date of deposit?
  - What are the advantages of shopping online and how can you find and compare products
    easily?
  - What are the different forms of ownership in real estate and how do they work?
- source_sentence: ' If you''re starting the new year with credit card debt, focus
    on creating a plan for bringing the balances down. And remember to track your
    progress so you have a motivational boost to stick with it. Why is a Good Credit
    Score Important? A good credit score can open a variety of financial doors. Higher
    credit scores can allow you to qualify for premium credit cards with better rewards
    and perks. An excellent credit score can also help you qualify for certain loans
    and mortgages, or even get better interest rates on the loans that you qualify
    for. With poor or no credit history, many financial products may be unavailable.
    But if you start implementing these keyways to improve your credit score, youll
    be on track to a better credit score and all the benefits that come with it. Using
    a Citi Secured Mastercard If youre just starting your credit journey, it may be
    hard to see what credit products you can qualify for. A secured credit card like
    the Citi Secured Mastercard is a great entry'
  sentences:
  - What are the benefits of having a good credit score?
  - What is the purpose of the above information provided by Citi?
  - When is the Best Time to Apply for a Credit Card?
- source_sentence: ' decreased rate of return on the reinvestment of the proceeds
    received as a result of a payment on a Deposit prior to its scheduled maturity,  payment
    in cash of the Deposit principal prior to maturity in connection with the liquidation
    of an insured institution or the assumption of all or a portion of its deposit
    liabilities at a lower interest rate or  its 29 receipt of a decreased rate of
    return as compared to the return on the applicable securities, indices, currencies,
    intangibles, articles, commodities or goods or any other economic measure or instrument,
    including the occurrence or non-occurrence of any event. Preference in Right of
    Payment Federal legislation adopted in 1993 provides for a preference in right
    of payment of certain claims made in the liquidation or other resolution of any
    FDIC-insured depository institution. The statute requires claims to be paid in
    the following order: First, administrative expenses of the receiver; Second, any
    deposit liability of the institution; Third, any other general or senior liability
    of the'
  sentences:
  - How can I protect myself from fake Citi SMS texts and fraudulent money transfers?
  - What are the details required to transfer funds out of my account and what are
    the different types of payments available for transferring funds out of my account?
  - What is the mechanism for decreased rate of return on reinvestment of the proceeds
    received as a result of a payment on a Deposit prior to its scheduled maturity?
- source_sentence: ' Citigroup Inc. All rights reserved. Citi, Citi and Arc Design
    and other marks used herein are service marks of Citigroup Inc. or its affliates,
    used and registered throughout the world. 2164316 GTS26358 0223 Tips to Become
    a Smart Credit Card User Citi.com - ATM Branch - Open an Account - Espaol !Citibank
    LogoSearch!Search Citi.com Menu - Credit Cards - View All Credit Cards - 0 Intro
    APR Credit Cards - Balance Transfer Credit Cards - Cash Back Credit Cards - Rewards
    Credit Cards - See If You''re Pre-Selected - Small Business Credit Cards - Banking
    - Banking Overview - Checking - Savings - Certificates of Deposit - Banking IRAs
    - Rates - Small Business Banking - Lending - Personal Loans Lines of Credit -
    Mortgage - Home Equity - Small Business Lending - Investing - Investing with Citi
    - Self Directed Trading - Citigold - Credit Cards - Credit Knowledge Center -
    Understanding Credit Cards - Tips'
  sentences:
  - What are the tips to become a smart credit card user?
  - What information do we request and receive from you to explain transactions or
    attempted transactions in or through your account?
  - Who has permission from the primary cardholder to use the credit card account
    and receive their own card with their own name?
- source_sentence: ' and Arc Design is a registered service mark of Citigroup Inc.
    OpenInvestor is a service mark of Citigroup Inc. 1044398 GTS74053 0113 Trade Working
    Capital Viewpoints Navigating global uncertainty: Perspectives on supporting the
    healthcare supply chain November 2023 Treasury and Trade Solutions Foreword Foreword
    Since the inception of the COVID-19 pandemic, the healthcare industry has faced
    supply chain disruptions. The industry, which has a long tradition in innovation,
    continues to transform to meet the needs of an evolving environment. Pauline kXXXXX
    Unlocking the full potential within the healthcare industry Global Head, Trade
    requires continuous investment. As corporates plan for the Working Capital Advisory
    future, careful working capital management is essential to ensuring they get there.
    Andrew Betts Global head of TTS Trade Sales Client Management, Citi Bayo Gbowu
    Global Sector Lead, Trade Healthcare and Wellness Ian Kervick-Jimenez Trade Working
    Capital Advisory 2 Treasury and Trade Solutions The Working'
  sentences:
  - How can I manage my Citibank accounts through International Personal Bank U.S.,
    either via internet, text messages, or phone calls?
  - What are the registered service marks of Citigroup Inc?
  - What is the role of DXX jXXXX US Real Estate Total Return SM Index in determining,
    composing or calculating products?
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.49420289855072463
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6768115942028986
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7478260869565218
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8333333333333334
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.49420289855072463
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.22560386473429955
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14956521739130432
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08333333333333333
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.49420289855072463
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6768115942028986
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7478260869565218
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8333333333333334
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6585419708540992
      name: Cosine Ndcg@10
    - type: cosine_ndcg@100
      value: 0.6900535995185644
      name: Cosine Ndcg@100
    - type: cosine_mrr@10
      value: 0.6032240625718881
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6096261483024806
      name: Cosine Map@100
---

# SentenceTransformer based on BAAI/bge-base-en-v1.5

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("MugheesAwan11/bge-base-citi-dataset-detailed-6k-0_5k-e2")
# Run inference
sentences = [
    ' and Arc Design is a registered service mark of Citigroup Inc. OpenInvestor is a service mark of Citigroup Inc. 1044398 GTS74053 0113 Trade Working Capital Viewpoints Navigating global uncertainty: Perspectives on supporting the healthcare supply chain November 2023 Treasury and Trade Solutions Foreword Foreword Since the inception of the COVID-19 pandemic, the healthcare industry has faced supply chain disruptions. The industry, which has a long tradition in innovation, continues to transform to meet the needs of an evolving environment. Pauline kXXXXX Unlocking the full potential within the healthcare industry Global Head, Trade requires continuous investment. As corporates plan for the Working Capital Advisory future, careful working capital management is essential to ensuring they get there. Andrew Betts Global head of TTS Trade Sales Client Management, Citi Bayo Gbowu Global Sector Lead, Trade Healthcare and Wellness Ian Kervick-Jimenez Trade Working Capital Advisory 2 Treasury and Trade Solutions The Working',
    'What are the registered service marks of Citigroup Inc?',
    'What is the role of DXX jXXXX US Real Estate Total Return SM Index in determining, composing or calculating products?',
]
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   | 0.4942     |
| cosine_accuracy@3   | 0.6768     |
| cosine_accuracy@5   | 0.7478     |
| cosine_accuracy@10  | 0.8333     |
| cosine_precision@1  | 0.4942     |
| cosine_precision@3  | 0.2256     |
| cosine_precision@5  | 0.1496     |
| cosine_precision@10 | 0.0833     |
| cosine_recall@1     | 0.4942     |
| cosine_recall@3     | 0.6768     |
| cosine_recall@5     | 0.7478     |
| cosine_recall@10    | 0.8333     |
| cosine_ndcg@10      | 0.6585     |
| cosine_ndcg@100     | 0.6901     |
| cosine_mrr@10       | 0.6032     |
| **cosine_map@100**  | **0.6096** |

<!--
## 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: 6,201 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: 146 tokens</li><li>mean: 205.96 tokens</li><li>max: 289 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.75 tokens</li><li>max: 241 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | anchor                                                                                                                                        |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
  | <code> combined balances do not include: balances in delinquent accounts; balances that exceed your approved credit When Deposits Are Credited to an Account limit for any line of credit or credit card; or outstanding balances Deposits received before the end of a Business Day  will be credited to your account that day. However, there been established for the Citigold Account Package. Your may be a delay before these funds are available for your use. See combined monthly balance range will be determined by computing the Funds Availability at Citibank section of this Marketplace an average of your monthly balances for your linked accounts Addendum for more information. during the prior calendar month. Monthly service fees are applied only to accounts with a combined average monthly balance range under the specified limits starting two  statement cycles after account opening. Service fees assessed will appear as a charge on your next statement. 2 3 Combined Average Monthly Non- Per Special Circumstances Monthly Balance Service Citibank Check If a checking account is converted</code>                                                          | <code>What are the conditions for balances to be included in the combined balances?</code>                                                    |
  | <code> the first six months, your credit score may not be where you want it just yet. There are other factors that impact your credit score including the length of your credit file, your credit mix and your credit utilization. If youre hoping to repair a credit score that has been damaged by financial setbacks, the timelines can be longer. A year or two with regular, timely payments and good credit utilization can push your credit score up. However, bankruptcies, collections, and late payments can linger on your credit report for anywhere from seven to ten years. That said, you may not have to use a secured credit card throughout your entire credit building process. Your goal may be to repair your credit to the point where your credit score is good enough to make you eligible for an unsecured credit card. To that end, youll need to research factors such as any fees that apply to the unsecured credit cards youre considering. There is no quick fix to having a great credit score. Building good credit with a</code>                                                                                                                                | <code>What factors impact your credit score including the length of your credit file, your credit mix, and your credit utilization?</code>    |
  | <code> by the index sponsor of the Constituents when it calculated the hypothetical back-tested index levels for the Constituents. It is impossible to predict whether the Index will rise or fall. The actual future performance of the Index may bear no relation to the historical or hypothetical back-tested levels of the Index. The Index Administrator, which is our Affiliate, and the Index Calculation Agent May Exercise Judgments under Certain Circumstances in the Calculation of the Index. Although the Index is rules- based, there are certain circumstances under which the Index Administrator or Index Calculation Agent may be required to exercise judgment in calculating the Index, including the following: The Index Administrator will determine whether an ambiguity, error or omission has arisen and the Index Administrator may resolve such ambiguity, error or omission, acting in good faith and in a commercially reasonable manner, and may amend the Index Rules to reflect the resolution of the ambiguity, error or omission in a manner that is consistent with the commercial objective of the Index. The Index Calculation Agents calculations</code> | <code>What circumstances may require the Index Administrator or Index Calculation Agent to exercise judgment in calculating the Index?</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
      ],
      "matryoshka_weights": [
          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
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `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`: 1
- `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`: 2
- `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_768_cosine_map@100 |
|:-------:|:-------:|:-------------:|:----------------------:|
| 0.0515  | 10      | 0.7623        | -                      |
| 0.1031  | 20      | 0.6475        | -                      |
| 0.1546  | 30      | 0.4492        | -                      |
| 0.2062  | 40      | 0.3238        | -                      |
| 0.2577  | 50      | 0.2331        | -                      |
| 0.3093  | 60      | 0.2575        | -                      |
| 0.3608  | 70      | 0.3619        | -                      |
| 0.4124  | 80      | 0.1539        | -                      |
| 0.4639  | 90      | 0.1937        | -                      |
| 0.5155  | 100     | 0.241         | -                      |
| 0.5670  | 110     | 0.2192        | -                      |
| 0.6186  | 120     | 0.2553        | -                      |
| 0.6701  | 130     | 0.2438        | -                      |
| 0.7216  | 140     | 0.1916        | -                      |
| 0.7732  | 150     | 0.189         | -                      |
| 0.8247  | 160     | 0.1721        | -                      |
| 0.8763  | 170     | 0.2353        | -                      |
| 0.9278  | 180     | 0.1713        | -                      |
| 0.9794  | 190     | 0.2121        | -                      |
| 1.0     | 194     | -             | 0.6100                 |
| 1.0309  | 200     | 0.1394        | -                      |
| 1.0825  | 210     | 0.156         | -                      |
| 1.1340  | 220     | 0.1276        | -                      |
| 1.1856  | 230     | 0.0969        | -                      |
| 1.2371  | 240     | 0.0811        | -                      |
| 1.2887  | 250     | 0.0699        | -                      |
| 1.3402  | 260     | 0.0924        | -                      |
| 1.3918  | 270     | 0.0838        | -                      |
| 1.4433  | 280     | 0.064         | -                      |
| 1.4948  | 290     | 0.0624        | -                      |
| 1.5464  | 300     | 0.0837        | -                      |
| 1.5979  | 310     | 0.0881        | -                      |
| 1.6495  | 320     | 0.1065        | -                      |
| 1.7010  | 330     | 0.0646        | -                      |
| 1.7526  | 340     | 0.084         | -                      |
| 1.8041  | 350     | 0.0697        | -                      |
| 1.8557  | 360     | 0.0888        | -                      |
| 1.9072  | 370     | 0.0873        | -                      |
| 1.9588  | 380     | 0.0755        | -                      |
| **2.0** | **388** | **-**         | **0.6096**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- 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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