sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:20 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:Snowflake/snowflake-arctic-embed-l base_model:finetune:Snowflake/snowflake-arctic-embed-l model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: Snowflake/snowflake-arctic-embed-l
library_name: sentence-transformers
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:20
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What initiatives were implemented in the past year to improve communication
    between departments?
  sentences:
  - "with other departments. In the past year, we conducted monthly departmental meetings\
    \ and \nestablished communication channels to facilitate information sharing and\
    \ problem-solving. \n \nFare Collection and Fee Structure"
  - "Our fare collection system ensures fair and consistent fee collection from passengers.\
    \ The current fee \nstructure is as follows: \n \nRegular fare: $2.50 \nSenior\
    \ citizens and students: $1.50 \nChildren under 5 years old: Free \nFee collection\
    \ is primarily done through electronic payment methods, such as smart cards and\
    \ \nmobile payment apps. Drivers are responsible for ensuring correct fare collection\
    \ and providing \nreceipts upon request. \nRoute Information and Rules \nOur transportation\
    \ department operates multiple routes within the city. Route information, including\
    \ \nmaps, schedules, and stops, is available on our website and at designated\
    \ information centers."
  - "Our drivers are responsible for operating vehicles safely, following traffic\
    \ rules and regulations. They \nare required to hold a valid driver's license\
    \ and maintain a clean driving record. In the past year, our \ndrivers completed\
    \ over 2,000 hours of driving training to enhance their skills and knowledge.\
    \ \n \nRoute Planning and Optimization \nEfficient route planning is essential\
    \ for timely transportation services. Our department utilizes \nadvanced routing\
    \ software to optimize routes and minimize travel time. In the past year, we reduced\
    \ \nour average route duration by 15% through effective route planning and optimization\
    \ strategies. \n \nCustomer Service"
- source_sentence: What is the primary focus of the Transportation Department as outlined
    in the manual?
  sentences:
  - "for familiarizing themselves with the latest version of the manual. \n \nConclusion\
    \ \nThank you for reviewing the Transportation Department Policy Manual. Your\
    \ commitment to safety, \ncustomer service, and compliance plays a crucial role\
    \ in our department's success. If you have any \nquestions or need further information,\
    \ please reach out to your supervisor or the department \nmanager. Your dedication\
    \ and professionalism are appreciated."
  - "department. It provides guidelines to ensure safe, efficient, and customer-focused\
    \ transportation \nservices. Please read this manual carefully and consult with\
    \ your supervisor or the department \nmanager if you have any questions or need\
    \ further clarification. \n \nDepartment Overview \nThe Transportation Department\
    \ plays a critical role in providing reliable transportation services to \nour\
    \ customers. Our department consists of 50 drivers, 10 dispatchers, and 5 maintenance\
    \ \ntechnicians. In the past year, we transported over 500,000 passengers across\
    \ various routes, ensuring \ntheir safety and satisfaction. \n \nSafety and Vehicle\
    \ Maintenance \nSafety is our top priority. All vehicles undergo regular inspections\
    \ and maintenance to ensure they"
  - "department. It provides guidelines to ensure safe, efficient, and customer-focused\
    \ transportation \nservices. Please read this manual carefully and consult with\
    \ your supervisor or the department \nmanager if you have any questions or need\
    \ further clarification. \n \nDepartment Overview \nThe Transportation Department\
    \ plays a critical role in providing reliable transportation services to \nour\
    \ customers. Our department consists of 50 drivers, 10 dispatchers, and 5 maintenance\
    \ \ntechnicians. In the past year, we transported over 500,000 passengers across\
    \ various routes, ensuring \ntheir safety and satisfaction. \n \nSafety and Vehicle\
    \ Maintenance \nSafety is our top priority. All vehicles undergo regular inspections\
    \ and maintenance to ensure they"
- source_sentence: How often were departmental meetings conducted to address information
    sharing and problem-solving?
  sentences:
  - "with other departments. In the past year, we conducted monthly departmental meetings\
    \ and \nestablished communication channels to facilitate information sharing and\
    \ problem-solving. \n \nFare Collection and Fee Structure"
  - "Compliance with local, state, and federal regulations is crucial. Our drivers\
    \ are required to maintain \nup-to-date knowledge of transportation laws and regulations.\
    \ In the past year, we conducted 20 \ncompliance audits to ensure adherence to\
    \ regulatory requirements. \n \nTraining and Development \nContinuous training\
    \ and development are vital for our department's success. In the past year, our\
    \ \ndrivers completed over 100 hours of professional development training, focusing\
    \ on defensive \ndriving, customer service, and emergency preparedness. \n \n\
    Communication and Collaboration \nEffective communication and collaboration are\
    \ essential within the Transportation Department and"
  - "are in optimal condition. In the past year, we conducted 500 vehicle inspections,\
    \ identifying and \naddressing any maintenance issues promptly. Our drivers are\
    \ required to conduct pre-trip and post-\ntrip inspections to ensure the safety\
    \ of the vehicles and passengers. \n \nDriver Responsibilities"
- source_sentence: How can passengers access route information and schedules for the
    transportation department?
  sentences:
  - "Our fare collection system ensures fair and consistent fee collection from passengers.\
    \ The current fee \nstructure is as follows: \n \nRegular fare: $2.50 \nSenior\
    \ citizens and students: $1.50 \nChildren under 5 years old: Free \nFee collection\
    \ is primarily done through electronic payment methods, such as smart cards and\
    \ \nmobile payment apps. Drivers are responsible for ensuring correct fare collection\
    \ and providing \nreceipts upon request. \nRoute Information and Rules \nOur transportation\
    \ department operates multiple routes within the city. Route information, including\
    \ \nmaps, schedules, and stops, is available on our website and at designated\
    \ information centers."
  - "Passengers are expected to follow the rules and regulations while utilizing our\
    \ transportation \nservices, including: \n \nBoarding and exiting the vehicle\
    \ in an orderly manner. \nYielding seats to elderly, disabled, and pregnant passengers.\
    \ \nKeeping noise levels to a minimum. \nRefraining from eating, drinking, or\
    \ smoking onboard. \nUsing designated safety equipment, such as seat belts, if\
    \ available. \nReporting any suspicious activity or unattended items to the driver.\
    \ \nAmendments to the Policy Manual \nThis policy manual is subject to periodic\
    \ review and amendments. Any updates or changes will be \ncommunicated to employees\
    \ through email or departmental meetings. Employees are responsible"
  - "Passengers are expected to follow the rules and regulations while utilizing our\
    \ transportation \nservices, including: \n \nBoarding and exiting the vehicle\
    \ in an orderly manner. \nYielding seats to elderly, disabled, and pregnant passengers.\
    \ \nKeeping noise levels to a minimum. \nRefraining from eating, drinking, or\
    \ smoking onboard. \nUsing designated safety equipment, such as seat belts, if\
    \ available. \nReporting any suspicious activity or unattended items to the driver.\
    \ \nAmendments to the Policy Manual \nThis policy manual is subject to periodic\
    \ review and amendments. Any updates or changes will be \ncommunicated to employees\
    \ through email or departmental meetings. Employees are responsible"
- source_sentence: Who should you contact if you have questions or need further information
    regarding the Transportation Department Policy Manual?
  sentences:
  - "Transportation Department Policy Manual \n \nTable of Contents: \n \n• \nIntroduction\
    \ \n• \nDepartment Overview \n• \nSafety and Vehicle Maintenance \n• \nDriver\
    \ Responsibilities \n• \nRoute Planning and Optimization \n• \nCustomer Service\
    \ \n• \nIncident Reporting and Investigation \n• \nCompliance with Regulations\
    \ \n• \nTraining and Development \n• \nCommunication and Collaboration \n• \n\
    Fare Collection and Fee Structure \n• \nRoute Information and Rules \n• \nAmendments\
    \ to the Policy Manual \n• \nConclusion \nIntroduction \nWelcome to the Transportation\
    \ Department Policy Manual! This manual serves as a comprehensive \nguide to the\
    \ policies, procedures, and expectations for employees working in the transportation"
  - "Compliance with local, state, and federal regulations is crucial. Our drivers\
    \ are required to maintain \nup-to-date knowledge of transportation laws and regulations.\
    \ In the past year, we conducted 20 \ncompliance audits to ensure adherence to\
    \ regulatory requirements. \n \nTraining and Development \nContinuous training\
    \ and development are vital for our department's success. In the past year, our\
    \ \ndrivers completed over 100 hours of professional development training, focusing\
    \ on defensive \ndriving, customer service, and emergency preparedness. \n \n\
    Communication and Collaboration \nEffective communication and collaboration are\
    \ essential within the Transportation Department and"
  - "for familiarizing themselves with the latest version of the manual. \n \nConclusion\
    \ \nThank you for reviewing the Transportation Department Policy Manual. Your\
    \ commitment to safety, \ncustomer service, and compliance plays a crucial role\
    \ in our department's success. If you have any \nquestions or need further information,\
    \ please reach out to your supervisor or the department \nmanager. Your dedication\
    \ and professionalism are appreciated."
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.9375
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9791666666666666
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9375
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.32638888888888884
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9375
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9791666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.971848173216197
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9625
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9625
      name: Cosine Map@100
---

# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("deepali1021/finetuned_arctic_ft")
# Run inference
sentences = [
    'Who should you contact if you have questions or need further information regarding the Transportation Department Policy Manual?',
    "for familiarizing themselves with the latest version of the manual. \n \nConclusion \nThank you for reviewing the Transportation Department Policy Manual. Your commitment to safety, \ncustomer service, and compliance plays a crucial role in our department's success. If you have any \nquestions or need further information, please reach out to your supervisor or the department \nmanager. Your dedication and professionalism are appreciated.",
    'Transportation Department Policy Manual \n \nTable of Contents: \n \n• \nIntroduction \n• \nDepartment Overview \n• \nSafety and Vehicle Maintenance \n• \nDriver Responsibilities \n• \nRoute Planning and Optimization \n• \nCustomer Service \n• \nIncident Reporting and Investigation \n• \nCompliance with Regulations \n• \nTraining and Development \n• \nCommunication and Collaboration \n• \nFare Collection and Fee Structure \n• \nRoute Information and Rules \n• \nAmendments to the Policy Manual \n• \nConclusion \nIntroduction \nWelcome to the Transportation Department Policy Manual! This manual serves as a comprehensive \nguide to the policies, procedures, and expectations for employees working in the transportation',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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

* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9375     |
| cosine_accuracy@3   | 0.9792     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9375     |
| cosine_precision@3  | 0.3264     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9375     |
| cosine_recall@3     | 0.9792     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.9718** |
| cosine_mrr@10       | 0.9625     |
| cosine_map@100      | 0.9625     |

<!--
## 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: 20 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 20 samples:
  |         | sentence_0                                                                        | sentence_1                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 12 tokens</li><li>mean: 16.3 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 95.1 tokens</li><li>max: 122 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                         | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
  |:---------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What topics are covered in the Transportation Department Policy Manual?</code>               | <code>Transportation Department Policy Manual <br> <br>Table of Contents: <br> <br>• <br>Introduction <br>• <br>Department Overview <br>• <br>Safety and Vehicle Maintenance <br>• <br>Driver Responsibilities <br>• <br>Route Planning and Optimization <br>• <br>Customer Service <br>• <br>Incident Reporting and Investigation <br>• <br>Compliance with Regulations <br>• <br>Training and Development <br>• <br>Communication and Collaboration <br>• <br>Fare Collection and Fee Structure <br>• <br>Route Information and Rules <br>• <br>Amendments to the Policy Manual <br>• <br>Conclusion <br>Introduction <br>Welcome to the Transportation Department Policy Manual! This manual serves as a comprehensive <br>guide to the policies, procedures, and expectations for employees working in the transportation</code> |
  | <code>What is the purpose of the Transportation Department Policy Manual?</code>                   | <code>Transportation Department Policy Manual <br> <br>Table of Contents: <br> <br>• <br>Introduction <br>• <br>Department Overview <br>• <br>Safety and Vehicle Maintenance <br>• <br>Driver Responsibilities <br>• <br>Route Planning and Optimization <br>• <br>Customer Service <br>• <br>Incident Reporting and Investigation <br>• <br>Compliance with Regulations <br>• <br>Training and Development <br>• <br>Communication and Collaboration <br>• <br>Fare Collection and Fee Structure <br>• <br>Route Information and Rules <br>• <br>Amendments to the Policy Manual <br>• <br>Conclusion <br>Introduction <br>Welcome to the Transportation Department Policy Manual! This manual serves as a comprehensive <br>guide to the policies, procedures, and expectations for employees working in the transportation</code> |
  | <code>What is the primary focus of the Transportation Department as outlined in the manual?</code> | <code>department. It provides guidelines to ensure safe, efficient, and customer-focused transportation <br>services. Please read this manual carefully and consult with your supervisor or the department <br>manager if you have any questions or need further clarification. <br> <br>Department Overview <br>The Transportation Department plays a critical role in providing reliable transportation services to <br>our customers. Our department consists of 50 drivers, 10 dispatchers, and 5 maintenance <br>technicians. In the past year, we transported over 500,000 passengers across various routes, ensuring <br>their safety and satisfaction. <br> <br>Safety and Vehicle Maintenance <br>Safety is our top priority. All vehicles undergo regular inspections and maintenance to ensure they</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`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `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
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch | Step | cosine_ndcg@10 |
|:-----:|:----:|:--------------:|
| 1.0   | 2    | 0.8107         |
| 2.0   | 4    | 0.9292         |
| 3.0   | 6    | 0.9623         |
| 4.0   | 8    | 0.9712         |
| 5.0   | 10   | 0.9642         |
| 6.0   | 12   | 0.9642         |
| 7.0   | 14   | 0.9642         |
| 8.0   | 16   | 0.9642         |
| 9.0   | 18   | 0.9718         |
| 10.0  | 20   | 0.9718         |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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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