README.md

---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:127
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: nomic-ai/modernbert-embed-base
widget:
- source_sentence: What is the difference between traditional programming and ML?
  sentences:
  - Over the past few years, the field of ML has advanced rapidly, especially in the
    area of Natural Language Processing (NLP)—the ability of machines to understand
    and generate human language. At the forefront of this progress are Large Language
    Models (LLMs), such as OpenAI’s GPT (Generative Pre-trained Transformer), Google’s
    PaLM, and Meta’s LLaMA
  - . For example, integrating an LLM into a customer support chatbot might involve
    connecting it to a company’s internal knowledge base, enabling it to answer customer
    questions using accurate, up-to-date information.
  - A major subset of AI is Machine Learning (ML), which involves algorithms that
    learn from data rather than being explicitly programmed. Instead of writing detailed
    instructions for every task, ML models find patterns in large datasets and use
    these patterns to make predictions or decisions
- source_sentence: What is one of the tasks mentioned that involves creating new written
    content?
  sentences:
  - In summary, AI and ML form the foundation for intelligent automation, while LLMs
    represent a breakthrough in language understanding and generation. Integrating
    these models into real-world systems unlocks practical value, turning raw intelligence
    into tangible solutions
  - '8. Security and Compliance Integrations

    Some organizations are integrating LLMs to detect anomalies in text communications
    (e.g., phishing detection or policy violations). LLMs can analyze language usage
    and flag potentially suspicious behavior more flexibly than keyword-based filters.


    Challenges in LLM Integration

    Despite their promise, integrating LLMs comes with challenges:'
  - . These include text generation, summarization, translation, question answering,
    code generation, and more.
- source_sentence: What is one of the components mentioned alongside AI?
  sentences:
  - '2. Search Engines and Semantic Search

    Traditional keyword-based search systems are being enhanced or replaced by semantic
    search, where LLMs understand the meaning behind queries. Instead of just matching
    words, they interpret intent.'
  - For example, e-commerce websites can deploy LLM-powered assistants to help customers
    find products, track orders, or get personalized recommendations—much more effectively
    than traditional rule-based bots.
  - Introduction to AI, Machine Learning, LLMs, and Their Integration
- source_sentence: What is required to provide intelligent features within broader
    applications?
  sentences:
  - . For instance, a spam filter doesn’t just block emails with specific keywords—it
    learns from thousands of examples what spam typically looks like.
  - 'The Rise of LLM Integrations

    While LLMs are powerful on their own, their true potential is unlocked through
    integration—connecting these models with other software, services, or systems
    to provide intelligent features within broader applications.


    Here are some key ways LLMs are being integrated into the digital world:'
  - For instance, in a document management system, a user might type "policies about
    sick leave", and the system—integrated with an LLM—could retrieve documents discussing
    "medical leave", "employee absence", and "illness policies", even if those exact
    words weren’t used.
- source_sentence: What type of dialogues can LLMs simulate?
  sentences:
  - Companies are also experimenting with Retrieval-Augmented Generation (RAG)—a technique
    where LLMs are paired with document databases (e.g., vector stores like Supabase,
    Pinecone, or Weaviate) to answer questions with enterprise-specific knowledge.
  - . For example, integrating an LLM into a customer support chatbot might involve
    connecting it to a company’s internal knowledge base, enabling it to answer customer
    questions using accurate, up-to-date information.
  - '5. Education and Learning Platforms

    Educational tools like Khanmigo (from Khan Academy) and other tutoring platforms
    are leveraging LLMs to provide real-time help to students. LLMs can break down
    complex topics, provide feedback on writing, and simulate Socratic-style dialogues.'
pipeline_tag: sentence-similarity
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
model-index:
- name: Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.6666666666666666
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8
      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: 0.6666666666666666
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2666666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000007
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000003
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6666666666666666
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8
      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: 0.8310827786456928
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7766666666666667
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7766666666666667
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6666666666666666
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8666666666666667
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6666666666666666
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2666666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17333333333333337
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000003
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6666666666666666
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8666666666666667
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8203966331432972
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7651851851851852
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7651851851851852
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6666666666666666
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8666666666666667
      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: 0.6666666666666666
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28888888888888886
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000007
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000003
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6666666666666666
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8666666666666667
      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: 0.8357043414408
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7822222222222223
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7822222222222223
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.5333333333333333
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7333333333333333
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9333333333333333
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5333333333333333
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2444444444444445
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16000000000000003
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09333333333333335
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5333333333333333
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7333333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9333333333333333
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7203966331432973
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6540740740740741
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6592022792022793
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.4666666666666667
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6666666666666666
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8666666666666667
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4666666666666667
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.22222222222222224
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16000000000000003
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08666666666666668
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.4666666666666667
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6666666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8666666666666667
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6507228370099043
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5822222222222223
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.58890559732665
      name: Cosine Map@100
---

# Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base). 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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 768 dimensions
- **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': 1024, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("Nuf-hugginface/modernbert-embed-quickb")
# Run inference
sentences = [
    'What type of dialogues can LLMs simulate?',
    '5. Education and Learning Platforms\nEducational tools like Khanmigo (from Khan Academy) and other tutoring platforms are leveraging LLMs to provide real-time help to students. LLMs can break down complex topics, provide feedback on writing, and simulate Socratic-style dialogues.',
    '. For example, integrating an LLM into a customer support chatbot might involve connecting it to a company’s internal knowledge base, enabling it to answer customer questions using accurate, up-to-date information.',
]
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

* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1   | 0.6667     | 0.6667     | 0.6667     | 0.5333     | 0.4667     |
| cosine_accuracy@3   | 0.8        | 0.8        | 0.8667     | 0.7333     | 0.6667     |
| cosine_accuracy@5   | 1.0        | 0.8667     | 1.0        | 0.8        | 0.8        |
| cosine_accuracy@10  | 1.0        | 1.0        | 1.0        | 0.9333     | 0.8667     |
| cosine_precision@1  | 0.6667     | 0.6667     | 0.6667     | 0.5333     | 0.4667     |
| cosine_precision@3  | 0.2667     | 0.2667     | 0.2889     | 0.2444     | 0.2222     |
| cosine_precision@5  | 0.2        | 0.1733     | 0.2        | 0.16       | 0.16       |
| cosine_precision@10 | 0.1        | 0.1        | 0.1        | 0.0933     | 0.0867     |
| cosine_recall@1     | 0.6667     | 0.6667     | 0.6667     | 0.5333     | 0.4667     |
| cosine_recall@3     | 0.8        | 0.8        | 0.8667     | 0.7333     | 0.6667     |
| cosine_recall@5     | 1.0        | 0.8667     | 1.0        | 0.8        | 0.8        |
| cosine_recall@10    | 1.0        | 1.0        | 1.0        | 0.9333     | 0.8667     |
| **cosine_ndcg@10**  | **0.8311** | **0.8204** | **0.8357** | **0.7204** | **0.6507** |
| cosine_mrr@10       | 0.7767     | 0.7652     | 0.7822     | 0.6541     | 0.5822     |
| cosine_map@100      | 0.7767     | 0.7652     | 0.7822     | 0.6592     | 0.5889     |

<!--
## 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: 127 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 127 samples:
  |         | anchor                                                                            | positive                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 8 tokens</li><li>mean: 13.28 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 53.34 tokens</li><li>max: 86 tokens</li></ul> |
* Samples:
  | anchor                                                                            | positive                                                                                                                                                                                                                                                                                                      |
  |:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What task mentioned is related to providing answers to inquiries?</code>    | <code>. These include text generation, summarization, translation, question answering, code generation, and more.</code>                                                                                                                                                                                      |
  | <code>What do LLMs learn to work effectively?</code>                              | <code>LLMs work by learning statistical relationships between words and phrases, allowing them to predict and generate language that feels natural. The power of these models lies not only in their size but also in the diversity of tasks they can perform with little to no task-specific training</code> |
  | <code>In which industries is the generalization ability considered useful?</code> | <code>. This generalization ability makes them incredibly useful across industries—from customer service and education to software development and healthcare.</code>                                                                                                                                         |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 4
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `tf32`: False
- `load_best_model_at_end`: True
- `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`: 4
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `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
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step   | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 1.0     | 4      | -             | 0.7790                 | 0.7120                 | 0.7474                 | 0.6321                 | 0.5684                |
| 2.0     | 8      | -             | 0.8275                 | 0.7966                 | 0.8091                 | 0.6904                 | 0.6102                |
| 2.5     | 10     | 13.4453       | -                      | -                      | -                      | -                      | -                     |
| 3.0     | 12     | -             | 0.8311                 | 0.8204                 | 0.8357                 | 0.7178                 | 0.6557                |
| **4.0** | **16** | **-**         | **0.8311**             | **0.8204**             | **0.8357**             | **0.7204**             | **0.6507**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.6
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cpu
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.1

## 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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