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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5822
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: nomic-ai/modernbert-embed-base
widget:
- source_sentence: >-
this information about the two documents withheld in part under the
deliberative-process
128
privilege in No. 11-444, see First Lutz Decl. Ex. DD at 17, 141, but the
descriptions of the
decisionmaking authority are generic, stating that the withheld information
is a “recommendation
from 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
contrato para el Departamento de Producción de tal corporación.
Adujo que, no se encontraba en la obligación de solicitar
autorización a la parte apelada para utilizar su imagen, ya que se le
había pagado por la producción de múltiples videos publicitarios
para el uso de las empresas.
Luego
de
varias
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
contend, violates Section 125.8(e) because it purportedly subjects protégés
to heightened
evaluation criteria as compared to offerors generally and makes it harder
for mentor-protégé JVs
to 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.
11-445 that “[s]ome of the records for which information has been withheld
pursuant to
Exemption (b)(5) contain confidential communications between CIA staff and
attorneys within
the 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,
2023).
5
protégé must demonstrate that the added mentor-protégé relationship will not
adversely affect the
development of either protégé firm (e.g., the second firm may not be a
competitor of the first
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?
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: 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
datasets:
- AdamLucek/legal-rag-positives-synthetic
---
# 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]
```
<!--
### 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>
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<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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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 |
<!--
## 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.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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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}
}
``` |