sentence-transformers safetensors modernbert sentence-similarity feature-extraction generated_from_trainer dataset_size:5822 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en dataset:AdamLucek/legal-rag-positives-synthetic arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:nomic-ai/modernbert-embed-base base_model:finetune:nomic-ai/modernbert-embed-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: nomic-ai/modernbert-embed-base
datasets:
- AdamLucek/legal-rag-positives-synthetic
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_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5822
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: "this information about the two documents withheld in part under\
    \ the deliberative-process \n128 \n \nprivilege in No. 11-444, see First Lutz\
    \ Decl. Ex. DD at 17, 141, but the descriptions of the \ndecisionmaking authority\
    \ are generic, stating that the withheld information is a “recommendation \nfrom\
    \ the [FOIA] analyst to his/her supervisor,” id. at 17, and a “recommendation\
    \ from the"
  sentences:
  - What did the plaintiff assert about the CIA's inaccurate representations?
  - What type of document is mentioned as an exhibit in conjunction with the withheld
    documents?
  - ¿Qué ámbito jurisdiccional es mencionado en el contexto de derechos sobre la propia
    imagen?
- source_sentence: "Artificial Intelligence, Corp. y que prestaba servicios mediante\
    \ \ncontrato para el Departamento de Producción de tal corporación. \nAdujo que,\
    \ no se encontraba en la obligación de solicitar \nautorización a la parte apelada\
    \ para utilizar su imagen, ya que se le \nhabía pagado por la producción de múltiples\
    \ videos publicitarios \npara el uso de las empresas.  \nLuego \nde \nvarias \n\
    incidencias"
  sentences:
  - Who has the burden to provide a sufficient record on appeal?
  - ¿Para qué departamento prestaba servicios Artificial Intelligence, Corp. según
    el contrato?
  - What section numbers are referenced for further information?
- source_sentence: "submission by protégé firms.  SHS MJAR at 28–30; VCH MJAR at 28–30\
    \ (same).  This, Plaintiffs \ncontend, violates Section 125.8(e) because it purportedly\
    \ subjects protégés to heightened \nevaluation criteria as compared to offerors\
    \ generally and makes it harder for mentor-protégé JVs \nto compete against more\
    \ experienced firms with larger portfolios of past work.  SHS MJAR at 28–"
  sentences:
  - On what date were the plaintiff's petition, complaint, and trial court's order
    filed?
  - What section do Plaintiffs contend is violated?
  - What is the amount of pages the party seeks to withhold?
- source_sentence: "Beginning with the CIA’s submissions, the CIA states in its declaration\
    \ submitted in No. \n11-445 that “[s]ome of the records for which information\
    \ has been withheld pursuant to \nExemption (b)(5) contain confidential communications\
    \ between CIA staff and attorneys within \nthe CIA’s Office of General Counsel\
    \ about the processing of certain FOIA requests.”  Third Lutz"
  sentences:
  - What is the subject of the confidential communications mentioned in the document?
  - Which rule number is associated with the responsibilities regarding nonlawyer
    assistants?
  - ¿Qué número de referencia tiene el documento?
- source_sentence: "contracting/contracting-assistance-programs/sba-mentor-protege-program\
    \ (last visited Apr. 19, \n2023). \n5 \n \nprotégé must demonstrate that the added\
    \ mentor-protégé relationship will not adversely affect the \ndevelopment of either\
    \ protégé firm (e.g., the second firm may not be a competitor of the first \n\
    firm).”  13 C.F.R. § 125.9(b)(3)."
  sentences:
  - What discretion do district courts have regarding a defendant’s invocation of
    FOIA exemptions?
  - What must the protégé demonstrate about the mentor-protégé relationship?
  - Which exemptions are mentioned in relation to the plaintiff's accusations?
model-index:
- name: ModernBERT Embed base Legal Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.5285935085007728
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5718701700154559
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6646058732612056
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7310664605873262
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5285935085007728
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.5141679546625451
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.3941267387944359
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.2329211746522411
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17877382792375063
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4894384337970118
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6120556414219475
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7184441009788768
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6300476733345887
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5741100561811532
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6186392686743281
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.5162287480680062
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5486862442040186
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6414219474497682
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7171561051004637
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5162287480680062
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.4981968057702215
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.38083462132921175
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.22720247295208656
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17400824317362185
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.47346728490468826
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5910613086038125
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.702344152498712
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6137901932050573
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5592913569343243
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6021884440021203
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.482225656877898
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5285935085007728
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.598145285935085
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.678516228748068
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.482225656877898
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.46986089644513135
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.35857805255023184
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.21468315301391033
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.16267387944358577
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4492529623905203
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5569294178258629
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6642194744976816
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5781404945062661
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5249122936139936
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5698418441661705
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.41576506955177744
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4435857805255023
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5363214837712519
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6105100463678517
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.41576506955177744
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3992787223080887
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.31282843894899537
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.19242658423493045
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14258114374034003
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3835651725914477
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.48776403915507466
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5963420917053066
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5108672198469205
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4573213365717227
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5029873598412773
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.312210200927357
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3508500772797527
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.43585780525502316
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.47913446676970634
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.312210200927357
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3091190108191654
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.250386398763524
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.14976816074188565
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10497166409067489
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2954662545079856
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3930963420917053
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.46805770221535287
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.39563928784117025
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3508985304580356
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3939277813526489
      name: Cosine Map@100
---

# ModernBERT Embed base Legal Matryoshka

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) on the [AdamLucek/legal-rag-positives-synthetic](https://huggingface.co/datasets/AdamLucek/legal-rag-positives-synthetic) dataset. 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 92168cbee600b1abbfc10842aba988aa69572291 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [AdamLucek/legal-rag-positives-synthetic](https://huggingface.co/datasets/AdamLucek/legal-rag-positives-synthetic)
- **Language:** en
- **License:** apache-2.0

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, '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("AdamLucek/ModernBERT-embed-base-legal-MRL")
# Run inference
sentences = [
    'contracting/contracting-assistance-programs/sba-mentor-protege-program (last visited Apr. 19, \n2023). \n5 \n \nprotégé must demonstrate that the added mentor-protégé relationship will not adversely affect the \ndevelopment of either protégé firm (e.g., the second firm may not be a competitor of the first \nfirm).”  13 C.F.R. § 125.9(b)(3).',
    'What must the protégé demonstrate about the mentor-protégé relationship?',
    'What discretion do district courts have regarding a defendant’s invocation of FOIA exemptions?',
]
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]
```

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</details>
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You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## 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   | 0.5286   | 0.5162     | 0.4822     | 0.4158     | 0.3122     |
| cosine_accuracy@3   | 0.5719   | 0.5487     | 0.5286     | 0.4436     | 0.3509     |
| cosine_accuracy@5   | 0.6646   | 0.6414     | 0.5981     | 0.5363     | 0.4359     |
| cosine_accuracy@10  | 0.7311   | 0.7172     | 0.6785     | 0.6105     | 0.4791     |
| cosine_precision@1  | 0.5286   | 0.5162     | 0.4822     | 0.4158     | 0.3122     |
| cosine_precision@3  | 0.5142   | 0.4982     | 0.4699     | 0.3993     | 0.3091     |
| cosine_precision@5  | 0.3941   | 0.3808     | 0.3586     | 0.3128     | 0.2504     |
| cosine_precision@10 | 0.2329   | 0.2272     | 0.2147     | 0.1924     | 0.1498     |
| cosine_recall@1     | 0.1788   | 0.174      | 0.1627     | 0.1426     | 0.105      |
| cosine_recall@3     | 0.4894   | 0.4735     | 0.4493     | 0.3836     | 0.2955     |
| cosine_recall@5     | 0.6121   | 0.5911     | 0.5569     | 0.4878     | 0.3931     |
| cosine_recall@10    | 0.7184   | 0.7023     | 0.6642     | 0.5963     | 0.4681     |
| **cosine_ndcg@10**  | **0.63** | **0.6138** | **0.5781** | **0.5109** | **0.3956** |
| cosine_mrr@10       | 0.5741   | 0.5593     | 0.5249     | 0.4573     | 0.3509     |
| cosine_map@100      | 0.6186   | 0.6022     | 0.5698     | 0.503      | 0.3939     |

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## Training Details

#### [AdamLucek/legal-rag-positives-synthetic](https://huggingface.co/datasets/AdamLucek/legal-rag-positives-synthetic)

* Dataset: [AdamLucek/legal-rag-positives-synthetic](https://huggingface.co/datasets/AdamLucek/legal-rag-positives-synthetic)
* Size: 5,822 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: 15 tokens</li><li>mean: 97.6 tokens</li><li>max: 153 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 16.68 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                       | anchor                                                                                  |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
  | <code>infrastructure security information,” the information at issue must, “if disclosed . . . reveal vulner-<br>abilities in Department of Defense critical infrastructure.”  10 U.S.C. § 130e(f).  The closest the <br>Department comes is asserting that the information “individually or in the aggregate, would enable</code>                                                                                             | <code>What type of information must reveal vulnerabilities if disclosed?</code>         |
  | <code>they have bid.”  Oral Arg. Tr. at 42:18–20.  Plaintiffs also assert that, should this Court require the <br>Polaris Solicitations to consider price at the IDIQ level, such an adjustment “adds a solicitation <br>requirement that would necessarily change the overall structure of the evaluation” GSA must <br>perform in awarding the IDIQ contracts.  Oral Arg. Tr. at 43:3–5; see supra Discussion Section</code> | <code>Where in the document can further discussion about the assertion be found?</code> |
  | <code>otra parte. Fernández v. San Juan Cement Co., Inc., 118 DPR 713, <br>718-719 (1987).   Nuestro más Alto Foro ha dispuesto que, la <br>facultad de imponer honorarios de abogados es la mejor arma que <br> <br>22 Id. <br>23 Andamios de PR v. Newport Bonding, 179 DPR 503, 520 (2010); Pérez Rodríguez <br>v. López Rodríguez, supra; SLG González -Figueroa v. Pacheco Romero, supra;</code>                          | <code>What case is cited with the reference number 118 DPR 713?</code>                  |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `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`: 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`: 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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8791     | 10     | 5.6528        | -                      | -                      | -                      | -                      | -                     |
| 1.0        | 12     | -             | 0.5926                 | 0.5753                 | 0.5457                 | 0.4687                 | 0.3455                |
| 1.7033     | 20     | 2.4543        | -                      | -                      | -                      | -                      | -                     |
| 2.0        | 24     | -             | 0.6195                 | 0.6066                 | 0.5778                 | 0.4998                 | 0.3828                |
| 2.5275     | 30     | 1.7455        | -                      | -                      | -                      | -                      | -                     |
| 3.0        | 36     | -             | 0.6292                 | 0.6135                 | 0.5765                 | 0.5057                 | 0.3928                |
| 3.3516     | 40     | 1.5499        | -                      | -                      | -                      | -                      | -                     |
| **3.7033** | **44** | **-**         | **0.63**               | **0.6138**             | **0.5781**             | **0.5109**             | **0.3956**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- 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}
}
```