sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:64 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
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:64
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'QUESTION #2\n...\n\nContext:\n[CONCLUSION]\n\nAs we wrap up today''s
    discussion, it''s clear that blockchain technology holds immense promise beyond
    cryptocurrencies. By addressing scalability, regulatory, and security challenges,
    while leveraging its diverse applications, industries can unlock the full potential
    of blockchain technology. Remember, collaboration, research, education, and innovation
    are key to harnessing the power of blockchain for a more secure and efficient
    future.\n        \n\n\nRenewable Energy Breakthroughs\nTopic: What are the latest
    breakthroughs in renewable energy technology?\n\n[INTRO]\n", additional_kwargs={},
    response_metadata={})]'
  sentences:
  - "[CONCLUSION]\n\nAs we wrap up today's discussion, it's clear that blockchain\
    \ technology holds immense promise beyond cryptocurrencies. By addressing scalability,\
    \ regulatory, and security challenges, while leveraging its diverse applications,\
    \ industries can unlock the full potential of blockchain technology. Remember,\
    \ collaboration, research, education, and innovation are key to harnessing the\
    \ power of blockchain for a more secure and efficient future.\n        \n\n\n\
    Renewable Energy Breakthroughs\nTopic: What are the latest breakthroughs in renewable\
    \ energy technology?\n\n[INTRO]"
  - 'The cost considerations associated with sustainable fashion can hinder accessibility
    for lower-income consumers, potentially widening disparities in the industry.
    Moreover, greenwashing practices by some fast fashion brands mislead consumers,
    making it hard to distinguish truly sustainable options.


    [POSITIVE ASPECTS]


    Despite these challenges, believers in sustainable fashion see a silver lining.
    Sustainable materials like organic cotton and hemp offer a path to reduce resource
    usage and carbon footprints. By embracing renewable energy sources and engaging
    consumers to prioritize quality over quantity, the industry can drive positive
    change.


    [CONCLUSION]'
  - '[CONCLUSION]


    In conclusion, the debate on human intelligence is multifaceted, encompassing
    evolutionary, social, ethical, and psychological dimensions. By exploring diverse
    perspectives and considering the opportunities and risks associated with human
    cognition, we can pave the way for growth and improvement.


    [TAKEAWAYS]


    As we reflect on today''s discussion, remember to approach discussions on intelligence
    with empathy and respect, embrace innovation while exercising prudence, and seek
    opportunities for personal and societal growth. By understanding the complexities
    of human cognition and behavior, we can strive towards a more inclusive and intellectually
    vibrant future.'
- source_sentence: 'QUESTION #2\n...\n\nContext:\n'
  sentences:
  - "1. Stay updated on advancements in Post-Quantum Cryptography and Quantum Key\
    \ Distribution.\n2. Invest in research and development of quantum-resistant encryption\
    \ techniques.\n3. Collaborate with industry stakeholders to establish robust security\
    \ standards.\n4. Plan a phased transition towards quantum-safe protocols to ensure\
    \ compatibility with current systems.\n        \n\n\nEnvironmental Impact of Fast\
    \ Fashion\nTopic: Explain the environmental impact of fast fashion and sustainable\
    \ alternatives\n\n[INTRO]"
  - 'The cost considerations associated with sustainable fashion can hinder accessibility
    for lower-income consumers, potentially widening disparities in the industry.
    Moreover, greenwashing practices by some fast fashion brands mislead consumers,
    making it hard to distinguish truly sustainable options.


    [POSITIVE ASPECTS]


    Despite these challenges, believers in sustainable fashion see a silver lining.
    Sustainable materials like organic cotton and hemp offer a path to reduce resource
    usage and carbon footprints. By embracing renewable energy sources and engaging
    consumers to prioritize quality over quantity, the industry can drive positive
    change.


    [CONCLUSION]'
  - 'Let''s start with the skeptic''s viewpoint. When examining the information related
    to human intelligence, it''s essential to consider the evolutionary perspective.
    The study suggesting a decline in human cognition over time raises crucial questions
    about intelligence trends. However, we must critically assess the study''s methodology
    and sample size to validate its findings. How can we ensure the accuracy of such
    studies and their implications for human cognition?


    [Transition to Subjectivity in Judging Intelligence]'
- source_sentence: 'QUESTION #2\n...\n\nContext:\nQuantum Computing and Cryptography\nTopic:
    What are the latest developments in quantum computing and how will they impact
    cryptography?\n\nWelcome to the Quantum Computing and Cryptography podcast, where
    we delve into the latest developments shaping the future of data security. Today,
    we''re exploring the impact of quantum computing on cryptography, weighing the
    opportunities and challenges it presents.\n\n[SKEPTIC PERSPECTIVE]\n", additional_kwargs={},
    response_metadata={})]'
  sentences:
  - '[INTRO]


    Welcome to our podcast where we delve into the environmental impact of fast fashion
    and explore sustainable alternatives. Today, we''ll navigate through the complexities
    of this issue by presenting a balanced debate between skeptics and believers of
    sustainable fashion.


    [SKEPTIC PERSPECTIVE]


    Let''s start by examining the skeptic''s viewpoint. Fast fashion undeniably poses
    a significant environmental threat, with its resource depletion, greenhouse gas
    emissions, and chemical pollution. While sustainable alternatives offer hope,
    they come with their own set of challenges.'
  - 'Quantum Computing and Cryptography

    Topic: What are the latest developments in quantum computing and how will they
    impact cryptography?


    Welcome to the Quantum Computing and Cryptography podcast, where we delve into
    the latest developments shaping the future of data security. Today, we''re exploring
    the impact of quantum computing on cryptography, weighing the opportunities and
    challenges it presents.


    [SKEPTIC PERSPECTIVE]'
  - "Green hydrogen production offers a revolutionary way to store excess renewable\
    \ energy and decarbonize various sectors. By leveraging renewable sources, we\
    \ can address energy intermittency issues and foster a cleaner energy ecosystem.\n\
    \n[CONCLUSION]\n\nAs we navigate the complexities of integrating the latest renewable\
    \ energy technologies, it's essential to address key questions and find solutions\
    \ for a sustainable and resilient energy future. By collaborating, investing in\
    \ research, and advocating for supportive policies, we can accelerate the transition\
    \ to a greener and brighter future.\n        \n\n\nSpace Exploration Benefits\n\
    Topic: How does space exploration benefit everyday life on Earth?\n\n[INTRO]"
- source_sentence: 'QUESTION #2\n...\n\nContext:\nOn the flip side, blockchain technology
    offers a myriad of opportunities in cybersecurity, data storage, smart contracts,
    supply chain management, healthcare, and finance. Its decentralized and secure
    nature enhances data security, transparency, and efficiency in various sectors.
    By leveraging these applications, industries can revolutionize their operations
    and drive progress and trust.\n\n[CONCLUSION]\n'', additional_kwargs={}, response_metadata={})]'
  sentences:
  - 'On the flip side, blockchain technology offers a myriad of opportunities in cybersecurity,
    data storage, smart contracts, supply chain management, healthcare, and finance.
    Its decentralized and secure nature enhances data security, transparency, and
    efficiency in various sectors. By leveraging these applications, industries can
    revolutionize their operations and drive progress and trust.


    [CONCLUSION]'
  - 'The sources we''ve examined underscore the grave consequences of microplastics
    on marine life. These tiny particles cause physical harm, toxicity, and pose a
    threat through entanglement and ingestion. The widespread presence of microplastics
    in the ocean not only endangers marine organisms but also raises concerns for
    the broader environment and human health.


    [BELIEVER PERSPECTIVE]


    While the impact of microplastics is indeed alarming, there are avenues for positive
    change. Researchers are actively studying microplastics, paving the way for innovative
    cleanup technologies. Increasing public awareness and education can drive behavioral
    changes, while regulations and policies can limit plastic usage.


    [CONCLUSION]'
  - '[INTRO]


    Welcome to the Renewable Energy Revolution podcast, where we explore the latest
    breakthroughs in renewable energy technology. Today, we dive into a debate between
    skeptics and believers about the opportunities and risks associated with these
    innovations.


    [SKEPTIC PERSPECTIVE]


    The latest breakthroughs in renewable energy technology hold promise for a sustainable
    future, but it''s crucial to dissect the challenges they bring. Green hydrogen
    production, for instance, faces hurdles due to high costs and infrastructure demands.
    Scaling up efficiently is key to its success.


    [BELIEVER PERSPECTIVE]'
- source_sentence: 'QUESTION #1\n'
  sentences:
  - '[INTRO]


    Welcome to the Health Innovations podcast, where we explore the latest advances
    in medical research. Today, we dive into the topic of the most promising breakthroughs
    in cancer treatment. We''ll hear from two perspectives - one skeptical and one
    optimistic - to provide a balanced view on the opportunities and challenges in
    this field.


    [SKEPTIC PERSPECTIVE]


    While the advancements in medical research for cancer treatment are promising,
    we must acknowledge the hurdles that come with them. Immunotherapy faces issues
    with response variability and severe side effects that need careful management.
    Clinical trials may not always represent the diversity of the population, and
    long-term effects can differ from short-term benefits.'
  - 'On the other hand, our believer highlights the positive aspects and opportunities
    for growth in this space. Increased awareness and education about healthy social
    media habits, the potential for community support, the availability of digital
    wellbeing tools, positive content creation, and the therapeutic benefits of online
    platforms all offer avenues for promoting mental health and wellbeing.


    [CONCLUSION]'
  - "Green hydrogen production offers a revolutionary way to store excess renewable\
    \ energy and decarbonize various sectors. By leveraging renewable sources, we\
    \ can address energy intermittency issues and foster a cleaner energy ecosystem.\n\
    \n[CONCLUSION]\n\nAs we navigate the complexities of integrating the latest renewable\
    \ energy technologies, it's essential to address key questions and find solutions\
    \ for a sustainable and resilient energy future. By collaborating, investing in\
    \ research, and advocating for supportive policies, we can accelerate the transition\
    \ to a greener and brighter future.\n        \n\n\nSpace Exploration Benefits\n\
    Topic: How does space exploration benefit everyday life on Earth?\n\n[INTRO]"
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.5416666666666666
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.625
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7083333333333334
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9166666666666666
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5416666666666666
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.20833333333333334
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1416666666666667
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09166666666666667
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5416666666666666
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.625
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7083333333333334
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9166666666666666
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6893149724203478
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6220403439153439
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6293004449254449
      name: Cosine Map@100
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("dataera2013/midterm-small-model")
# Run inference
sentences = [
    'QUESTION #1\\n',
    'On the other hand, our believer highlights the positive aspects and opportunities for growth in this space. Increased awareness and education about healthy social media habits, the potential for community support, the availability of digital wellbeing tools, positive content creation, and the therapeutic benefits of online platforms all offer avenues for promoting mental health and wellbeing.\n\n[CONCLUSION]',
    "[INTRO]\n\nWelcome to the Health Innovations podcast, where we explore the latest advances in medical research. Today, we dive into the topic of the most promising breakthroughs in cancer treatment. We'll hear from two perspectives - one skeptical and one optimistic - to provide a balanced view on the opportunities and challenges in this field.\n\n[SKEPTIC PERSPECTIVE]\n\nWhile the advancements in medical research for cancer treatment are promising, we must acknowledge the hurdles that come with them. Immunotherapy faces issues with response variability and severe side effects that need careful management. Clinical trials may not always represent the diversity of the population, and long-term effects can differ from short-term benefits.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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   | 0.5417     |
| cosine_accuracy@3   | 0.625      |
| cosine_accuracy@5   | 0.7083     |
| cosine_accuracy@10  | 0.9167     |
| cosine_precision@1  | 0.5417     |
| cosine_precision@3  | 0.2083     |
| cosine_precision@5  | 0.1417     |
| cosine_precision@10 | 0.0917     |
| cosine_recall@1     | 0.5417     |
| cosine_recall@3     | 0.625      |
| cosine_recall@5     | 0.7083     |
| cosine_recall@10    | 0.9167     |
| **cosine_ndcg@10**  | **0.6893** |
| cosine_mrr@10       | 0.622      |
| cosine_map@100      | 0.6293     |

<!--
## 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: 64 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 64 samples:
  |         | sentence_0                                                                         | sentence_1                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 7 tokens</li><li>mean: 74.97 tokens</li><li>max: 200 tokens</li></ul> | <ul><li>min: 72 tokens</li><li>mean: 110.72 tokens</li><li>max: 140 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>QUESTION #1\n</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>Tech & Science Podcast Blog<br><br><br><br>Tech & Science Podcast Transcripts<br><br>Are Humans Dumb?<br>Topic: are humans dumb<br><br>[INTRO]<br><br>Welcome to our podcast where we delve into the intriguing question: Are humans dumb? Today, we will explore this topic from two contrasting perspectives - skepticism and belief. Let's navigate through the complexities of human cognition and behavior to uncover the opportunities, risks, key questions, and potential solutions surrounding this thought-provoking issue.<br><br>[SKEPTIC PERSPECTIVE]</code> |
  | <code>QUESTION #2\n...\n\nContext:\nTech & Science Podcast Blog\n\n\n\nTech & Science Podcast Transcripts\n\nAre Humans Dumb?\nTopic: are humans dumb\n\n[INTRO]\n\nWelcome to our podcast where we delve into the intriguing question: Are humans dumb? Today, we will explore this topic from two contrasting perspectives - skepticism and belief. Let's navigate through the complexities of human cognition and behavior to uncover the opportunities, risks, key questions, and potential solutions surrounding this thought-provoking issue.\n\n[SKEPTIC PERSPECTIVE]\n", additional_kwargs={}, response_metadata={})]</code> | <code>Tech & Science Podcast Blog<br><br><br><br>Tech & Science Podcast Transcripts<br><br>Are Humans Dumb?<br>Topic: are humans dumb<br><br>[INTRO]<br><br>Welcome to our podcast where we delve into the intriguing question: Are humans dumb? Today, we will explore this topic from two contrasting perspectives - skepticism and belief. Let's navigate through the complexities of human cognition and behavior to uncover the opportunities, risks, key questions, and potential solutions surrounding this thought-provoking issue.<br><br>[SKEPTIC PERSPECTIVE]</code> |
  | <code>QUESTION #1\n</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>Let's start with the skeptic's viewpoint. When examining the information related to human intelligence, it's essential to consider the evolutionary perspective. The study suggesting a decline in human cognition over time raises crucial questions about intelligence trends. However, we must critically assess the study's methodology and sample size to validate its findings. How can we ensure the accuracy of such studies and their implications for human cognition?<br><br>[Transition to Subjectivity in Judging Intelligence]</code>                       |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          192,
          96,
          48
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 5
- `per_device_eval_batch_size`: 5
- `num_train_epochs`: 5
- `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`: 5
- `per_device_eval_batch_size`: 5
- `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`: 5
- `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    | 13   | 0.6893         |
| 2.0    | 26   | 0.6893         |
| 3.0    | 39   | 0.6893         |
| 3.8462 | 50   | 0.6893         |
| 4.0    | 52   | 0.6893         |
| 5.0    | 65   | 0.6893         |


### Framework Versions
- Python: 3.13.1
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- 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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