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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1440
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: nomic-ai/modernbert-embed-base
widget:
- source_sentence: What section of the Code of Federal Regulations is quoted?
sentences:
- "and other legal relations of any interested party seeking such declaration.”\
\ 28 U.S.C. § 2201(a). \nThis statute “is not an independent source of federal\
\ jurisdiction”; rather, “the availability of \nsuch relief presupposes the existence\
\ of a judicially remediable right.” Schilling v. Rogers, 363 \nU.S. 666, 677\
\ (1960). The Court independently has jurisdiction here under the mandamus"
- "appropriate only when the nature of the work is sporadic and unpredictable so\
\ that a tour of duty \ncannot be regularly scheduled in advance.” Pl.’s Mem.\
\ at 18 (quoting 5 C.F.R. § 340.403(a)). \nThis regulation explicitly distinguishes\
\ “intermittent” status from “part-time” status, as it says \nthat “[w]hen an\
\ agency is able to schedule work in advance on a regular basis, it has an"
- "its discretion, a reviewing court looks to the trial court’s “stated justification\
\ for refusing to \nmodify” the order. Skolnick, 191 Ill. 2d at 226. \n \n \n\
In the case at bar, the one-sentence April 25 order did not provide any reasons\
\ at all. The \nlosing party drafted the order without any stated reasons, although\
\ a lack of stated reasons may"
- source_sentence: Which office was determined to be an agency in the Soucie case?
sentences:
- "inquiry”); Doe v. Skyline Automobiles, Inc., 375 F. Supp. 3d 401, 405-06 (S.D.N.Y.\
\ 2019) \n(“other factors must be taken into consideration and analyzed in comparison\
\ to the public’s \ninterest and the interests of the opposing parties”). \n \n\
\ \nIllinois has taken steps to protect individuals’ private information. Examples\
\ include the"
- "Aside from whether the Department’s “approach to artificial intelligence development\
\ and \nimplementation” should be considered “critical infrastructure,” the Department’s\
\ affidavit is \n \n \n5\ndeficient in showing that its withholdings qualify as\
\ “critical infrastructure security information” \nin other ways. For example,\
\ the affidavit fails to explain how the disclosure of the withheld infor-"
- "whether an entity wields “substantial independent authority”: investigative\
\ power and authority \nto make final and binding decisions. \nConsider first\
\ Soucie. The Circuit held that the Office of Science and Technology \n(“OST”)\
\ was an agency because, beyond advising the President, it had the “independent\
\ function"
- source_sentence: What is the appellant's burden on appeal?
sentences:
- "Defs.’ Reply at 7–8, 8 n.1. It cites Judicial Watch, Inc. v. Department of Energy,\
\ 412 F.3d 125 \n(D.C. Cir. 2005), which dealt with the records of employees that\
\ the Department of Energy \n(“DOE”) had detailed to the National Energy Policy\
\ Development Group (“NEPDG”). Id. at \n132. The Government quotes the court’s\
\ statement that “the records those employees created or"
- "records available for inspection and copying is a violation of 5 U.S.C. app.\
\ 2 § 10(b) and \nconstitutes a failure to perform a duty owed to EPIC within\
\ the meaning of 28 U.S.C. § 1361.” \nId. . Both counts seek “a writ of mandamus”\
\ compelling the Commission and its officers to \ncomply with FACA. Id. , 139.\
\ These counts make clear that EPIC seeks mandamus relief"
- "counsel now cannot fairly contend that the trial court did not consider all the\
\ facts, especially \nwhen [d]efendant’s counsel offers no court transcript to\
\ show otherwise.” On appeal, it is \ngenerally the appellant’s burden to provide\
\ the reviewing court with a sufficient record to \nestablish the error that he\
\ complains of. Webster v. Hartman, 195 Ill. 2d 426, 436 (2001). “[A]"
- source_sentence: What does the text refer to as a 'statutory distinction'?
sentences:
- "inconsistency in deeming the same entity an advisory committee and an agency.”\
\ Defs.’ Reply \nat 8. The problem, according to the Government, is that FACA\
\ generally requires disclosure of \nrecords, yet Exemption 5 would shield a portion\
\ of these records from public view, which would \nundermine FACA’s “purpose.”\
\ Id. at 8–9. Gates, Wolfe, and the 1988 OLC opinion echo this"
- "agencies are operating arms of government characterized by ‘substantial independent\
\ authority in \nthe exercise of specific functions.’” Disclosure of Advisory\
\ Comm. Deliberative Materials, 12 \nOp. O.L.C. 73, 81 (1988). This “statutory\
\ distinction,” it concludes, signifies that “advisory \ncommittees are not agencies.”\
\ Id."
- "the Hon. Israel A. Desierto, Judge, presiding. \n \n \nJudgment \nAffirmed. \n\
\ \nCounsel on \nAppeal \n \nVictor P. Henderson and Colin Quinn Commito, of Henderson\
\ Parks, \nLLC, of Chicago, for appellant. \n \nTamara N. Holder, Law Firm of\
\ Tamara N. Holder LLC, of Chicago, \nfor appellee. \n \n \n \nPanel \n \nPRESIDING\
\ JUSTICE ODEN JOHNSON delivered the judgment of \nthe court, with opinion."
- source_sentence: What do the newly enacted laws prohibit hospitals from doing regarding
sexual assault victims?
sentences:
- "exclusion for committees “composed wholly of . . . permanent part-time . . .\
\ employees.” 5 \nU.S.C. app. 2 § 3(2). \n32 \nA second, independent reason why\
\ the Commission does not fall within this exclusion is \nthat its members are\
\ not “part-time” federal employees. Instead, they are “intermittent” \nemployees.\
\ EPIC points to a regulation stating that “[a]n intermittent work schedule is"
- "committee, board, commission, council, conference, panel, task force, or other\
\ similar group, or \nany subcommittee or other subgroup thereof.” Id. § 3(2).\
\ Second, it must be “established by \nstatute or reorganization plan,” “established\
\ or utilized by the President,” or “established or \nutilized by one or more\
\ agencies.” Id. Third, it must be “established” or “utilized” “in the"
- "confidential advisors (735 ILCS 5/8-804(c) (West 2022)) and prohibit hospitals\
\ treating sexual \nassault victims from directly billing the victims for the\
\ services, communicating with victims \nabout a bill, or referring overdue bills\
\ to collection agencies or credit reporting agencies. 410 \nILCS 70/7.5(a)(1)-(4)\
\ (West 2022). These recently enacted laws encourage victims to report"
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: 0.51875
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.69375
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.75
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.83125
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.51875
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.23125
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14999999999999997
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08312499999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.51875
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.69375
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.75
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.83125
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.671534966140965
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6211160714285715
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6261949467277568
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.49375
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.73125
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.825
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.49375
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14625
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08249999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.49375
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.73125
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.825
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6607544642083831
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6085367063492064
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6146313607229802
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.4375
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6875
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.725
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.79375
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4375
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22916666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.145
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.079375
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4375
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6875
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.725
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.79375
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6224957341997419
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.566939484126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5740997074969412
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.40625
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.625
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.69375
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.775
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.40625
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.20833333333333331
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.13874999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07749999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.40625
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.625
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.69375
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.775
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5931742895464828
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5348859126984128
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5417826806767716
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.30625
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4875
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6875
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.30625
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16249999999999998
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06875
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.30625
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4875
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6875
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4854299754851493
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.42175347222222237
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4326739799760461
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("AdamLucek/modernbert-embed-quickb-video")
# Run inference
sentences = [
'What do the newly enacted laws prohibit hospitals from doing regarding sexual assault victims?',
'confidential advisors (735 ILCS 5/8-804(c) (West 2022)) and prohibit hospitals treating sexual \nassault victims from directly billing the victims for the services, communicating with victims \nabout a bill, or referring overdue bills to collection agencies or credit reporting agencies. 410 \nILCS 70/7.5(a)(1)-(4) (West 2022). These recently enacted laws encourage victims to report',
'exclusion for committees “composed wholly of . . . permanent part-time . . . employees.” 5 \nU.S.C. app. 2 § 3(2). \n32 \nA second, independent reason why the Commission does not fall within this exclusion is \nthat its members are not “part-time” federal employees. Instead, they are “intermittent” \nemployees. EPIC points to a regulation stating that “[a]n intermittent work schedule is',
]
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 | 0.5188 | 0.4938 | 0.4375 | 0.4062 | 0.3063 |
| cosine_accuracy@3 | 0.6937 | 0.7 | 0.6875 | 0.625 | 0.4875 |
| cosine_accuracy@5 | 0.75 | 0.7312 | 0.725 | 0.6937 | 0.6 |
| cosine_accuracy@10 | 0.8313 | 0.825 | 0.7937 | 0.775 | 0.6875 |
| cosine_precision@1 | 0.5188 | 0.4938 | 0.4375 | 0.4062 | 0.3063 |
| cosine_precision@3 | 0.2313 | 0.2333 | 0.2292 | 0.2083 | 0.1625 |
| cosine_precision@5 | 0.15 | 0.1462 | 0.145 | 0.1387 | 0.12 |
| cosine_precision@10 | 0.0831 | 0.0825 | 0.0794 | 0.0775 | 0.0688 |
| cosine_recall@1 | 0.5188 | 0.4938 | 0.4375 | 0.4062 | 0.3063 |
| cosine_recall@3 | 0.6937 | 0.7 | 0.6875 | 0.625 | 0.4875 |
| cosine_recall@5 | 0.75 | 0.7312 | 0.725 | 0.6937 | 0.6 |
| cosine_recall@10 | 0.8313 | 0.825 | 0.7937 | 0.775 | 0.6875 |
| **cosine_ndcg@10** | **0.6715** | **0.6608** | **0.6225** | **0.5932** | **0.4854** |
| cosine_mrr@10 | 0.6211 | 0.6085 | 0.5669 | 0.5349 | 0.4218 |
| cosine_map@100 | 0.6262 | 0.6146 | 0.5741 | 0.5418 | 0.4327 |
<!--
## 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: 1,440 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 15.14 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 57 tokens</li><li>mean: 97.82 tokens</li><li>max: 161 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What must the advisory committee make available for public inspection?</code> | <code>advisory committee shall be available for public inspection and copying . . . until the advisory <br>committee ceases to exist.” Id. § 10(b). Unlike FOIA, this provision looks forward. It requires <br>committees to take affirmative steps to make their records are public, even absent a request. <br>FACA’s definition of “advisory committee” has four parts. First, it includes “any</code> |
| <code>What did the landlords fail to alert the court about?</code> | <code>court documents containing fake citations, we conclude that <br>imposing monetary sanctions or dismissing this appeal would be <br>disproportionate to Al-Hamim’s violation of the Appellate Rules. <br> <br>23 <br>Further, in their answer brief, the landlords failed to alert this court <br>to the hallucinations in Al-Hamim’s opening brief and did not <br>request an award of attorney fees against Al-Hamim. Under the</code> |
| <code>On what date was the motion served on the plaintiff’s counsel?</code> | <code>also alleged (1) that plaintiff violated section 2-401(e) and (2) that she lacked good cause to <br>file anonymously because she signed an affidavit in her own name in another case with similar <br>allegations. The April 13 motion contains a “Certificate of Service” stating that it was served <br>on plaintiff’s counsel by e-mail on April 13.</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
- `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`: 8
- `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 | 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 | 3 | 0.6493 | 0.6372 | 0.5987 | 0.5536 | 0.4520 |
| 2.0 | 6 | 0.6685 | 0.6514 | 0.6208 | 0.5916 | 0.4859 |
| **2.7111** | **8** | **0.6715** | **0.6608** | **0.6225** | **0.5932** | **0.4854** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+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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