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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: "publicly announced on August 27, 2018 that it was writing down\
\ the carrying \nvalue of its investment in DR to “nil,” after acknowledging that\
\ DR’s failure to \nachieve FDA approval for its technology and missing “milestone\
\ and projected \nrevenue targets.”72 Although Senetas understood that such an\
\ action could \nnegatively affect DR’s future funding,73 it states that, as a\
\ publicly traded company"
sentences:
- What is the plaintiff required to do with the Complaint?
- What potential effect on DR did Senetas understand could result from writing down
the investment?
- What is the Federal Circuit case citation number for Shea v. United States?
- source_sentence: "has any subpoena or contempt authority, the sort of powers incident\
\ to Congress’s “broad power \nof inquiry.” Soucie, 448 F.2d at 1075 & n.27;\
\ see McGrain, 273 U.S. at 168–69, 180. \nGiven these considerations, the Commission\
\ does not exercise “substantial independent \nauthority.” Accord Flaherty v.\
\ Ross, 373 F. Supp. 3d 97, 106–10 (D.D.C. 2019).5 The upshot is"
sentences:
- Who had personal knowledge of the first and third events?
- What is said about the Commission's exercise of authority?
- What are the page references for the decision in Cole?
- source_sentence: "pending. The Court will begin by explaining why it denies the\
\ plaintiff’s motion for leave to file \na second amended complaint in No. 11-445.\
\ The Court will then discuss the plaintiff’s Motion \nfor Sanctions, filed in\
\ No. 11-443. Third, the Court will address the plaintiff’s two remaining \n\
policy-or-practice claims, which challenge the CIA’s Assignment of Rights Policy\
\ and"
sentences:
- What type of motion did the court deny the plaintiff?
- What policies are being challenged by the plaintiff’s remaining claims?
- What is cited in the location where the argument about crafting task orders is
made?
- source_sentence: "(11th ed. 2003). While Defendant correctly identifies that labor\
\ costs are often contemplated in \npricing firm fixed-price contracts, the same\
\ is true for a wide variety of other costs. See, e.g., \nLakeshore Eng’g Servs.,\
\ 748 F.3d at 1343 (“[T]he mechanism of pricing such jobs involves \nidentification\
\ of costs for those jobs, including labor, equipment, and materials . . . .”).\
\ \n60"
sentences:
- What other costs, besides labor, are involved in the pricing mechanism of jobs
according to Lakeshore Eng’g Servs.?
- What did the letters seek from the recipients regarding dmed.ai?
- To which community do the sensitive operations related to the withheld information
belong?
- source_sentence: "protests pursuant to 28 U.S.C. § 1491(b). See 28 U.S.C. § 1491(b).\
\ Section 1491(b)(1) grants the \n17 \n \ncourt jurisdiction over protests filed\
\ “by an interested party objecting to a solicitation by a Federal \nagency for\
\ bids or proposals for a proposed contract . . . or any alleged violation of\
\ statute or"
sentences:
- Which agency's declaration is mentioned?
- Under which U.S. Code section are the protests filed?
- What does the camera do when it begins and ends recording?
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: 0.5919629057187017
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6352395672333848
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7032457496136012
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7666151468315301
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5919629057187017
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.5682637815558991
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.42627511591962897
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.24080370942812984
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.20118495620814011
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5470118495620814
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6664090674909839
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.750772797527048
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6774170353263864
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6316503520522063
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6706536977021021
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.5857805255023184
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.616692426584235
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6908809891808346
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7666151468315301
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5857805255023184
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.5574446161772283
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4176197836166924
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.24173106646058728
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.19835136527563108
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5352910870685214
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.651468315301391
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7518031942297784
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6721586770579754
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6236285910551748
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.662035963048261
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.5672333848531684
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5873261205564142
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: 0.5672333848531684
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.538382277176713
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.40092735703245747
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.23075734157650693
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.19062339000515197
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5151983513652756
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6263523956723338
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.720504894384338
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6453735180059756
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6008856504992514
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6377344841740619
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.4992272024729521
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5301391035548686
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6136012364760433
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.678516228748068
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4992272024729521
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.47449768160741884
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.3653786707882534
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.215919629057187
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.16950025759917567
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.45814013395157127
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5705821741370428
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6683410613086037
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5891910709474154
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5385613699369493
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5782906402819172
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.36321483771251933
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.401854714064915
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.47295208655332305
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.527047913446677
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.36321483771251933
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.35136527563111797
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2782071097372488
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.16506955177743432
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12364760432766615
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.33912931478619274
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.43637300360638853
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5142967542503863
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.44443933760269555
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4003011457030003
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.44619919912465567
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 json 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 d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **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': 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("manishh16/modernbert-embed-base-legal-matryoshka-2")
# Run inference
sentences = [
'protests pursuant to 28 U.S.C. § 1491(b). See 28 U.S.C. § 1491(b). Section 1491(b)(1) grants the \n17 \n \ncourt jurisdiction over protests filed “by an interested party objecting to a solicitation by a Federal \nagency for bids or proposals for a proposed contract . . . or any alleged violation of statute or',
'Under which U.S. Code section are the protests filed?',
"Which agency's declaration is mentioned?",
]
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)
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</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
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 768
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.592 |
| cosine_accuracy@3 | 0.6352 |
| cosine_accuracy@5 | 0.7032 |
| cosine_accuracy@10 | 0.7666 |
| cosine_precision@1 | 0.592 |
| cosine_precision@3 | 0.5683 |
| cosine_precision@5 | 0.4263 |
| cosine_precision@10 | 0.2408 |
| cosine_recall@1 | 0.2012 |
| cosine_recall@3 | 0.547 |
| cosine_recall@5 | 0.6664 |
| cosine_recall@10 | 0.7508 |
| **cosine_ndcg@10** | **0.6774** |
| cosine_mrr@10 | 0.6317 |
| cosine_map@100 | 0.6707 |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 512
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5858 |
| cosine_accuracy@3 | 0.6167 |
| cosine_accuracy@5 | 0.6909 |
| cosine_accuracy@10 | 0.7666 |
| cosine_precision@1 | 0.5858 |
| cosine_precision@3 | 0.5574 |
| cosine_precision@5 | 0.4176 |
| cosine_precision@10 | 0.2417 |
| cosine_recall@1 | 0.1984 |
| cosine_recall@3 | 0.5353 |
| cosine_recall@5 | 0.6515 |
| cosine_recall@10 | 0.7518 |
| **cosine_ndcg@10** | **0.6722** |
| cosine_mrr@10 | 0.6236 |
| cosine_map@100 | 0.662 |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 256
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5672 |
| cosine_accuracy@3 | 0.5873 |
| cosine_accuracy@5 | 0.6646 |
| cosine_accuracy@10 | 0.7311 |
| cosine_precision@1 | 0.5672 |
| cosine_precision@3 | 0.5384 |
| cosine_precision@5 | 0.4009 |
| cosine_precision@10 | 0.2308 |
| cosine_recall@1 | 0.1906 |
| cosine_recall@3 | 0.5152 |
| cosine_recall@5 | 0.6264 |
| cosine_recall@10 | 0.7205 |
| **cosine_ndcg@10** | **0.6454** |
| cosine_mrr@10 | 0.6009 |
| cosine_map@100 | 0.6377 |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 128
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4992 |
| cosine_accuracy@3 | 0.5301 |
| cosine_accuracy@5 | 0.6136 |
| cosine_accuracy@10 | 0.6785 |
| cosine_precision@1 | 0.4992 |
| cosine_precision@3 | 0.4745 |
| cosine_precision@5 | 0.3654 |
| cosine_precision@10 | 0.2159 |
| cosine_recall@1 | 0.1695 |
| cosine_recall@3 | 0.4581 |
| cosine_recall@5 | 0.5706 |
| cosine_recall@10 | 0.6683 |
| **cosine_ndcg@10** | **0.5892** |
| cosine_mrr@10 | 0.5386 |
| cosine_map@100 | 0.5783 |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 64
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3632 |
| cosine_accuracy@3 | 0.4019 |
| cosine_accuracy@5 | 0.473 |
| cosine_accuracy@10 | 0.527 |
| cosine_precision@1 | 0.3632 |
| cosine_precision@3 | 0.3514 |
| cosine_precision@5 | 0.2782 |
| cosine_precision@10 | 0.1651 |
| cosine_recall@1 | 0.1236 |
| cosine_recall@3 | 0.3391 |
| cosine_recall@5 | 0.4364 |
| cosine_recall@10 | 0.5143 |
| **cosine_ndcg@10** | **0.4444** |
| cosine_mrr@10 | 0.4003 |
| cosine_map@100 | 0.4462 |
<!--
## 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
### Training Dataset
#### json
* Dataset: json
* 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: 26 tokens</li><li>mean: 96.76 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 16.59 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|
| <code>properly authenticated. See id. at 367, 19 A.3d at 429 (Harrell, J., dissenting). <br>Four years later, in Sublet, 442 Md. at 637-38, 113 A.3d at 697-98, we adopted the <br>reasonable juror test for social media evidence and applied it in the three cases that were <br>consolidated for purposes of the opinion: Sublet v. State, Harris v. State, and Monge-</code> | <code>How many years after the dissent did the adoption of the reasonable juror test occur?</code> |
| <code>to (1) a public-interest fee waiver, (2) the expedited processing of a request, or (3) the release of <br>information that implicates personal privacy, all are personal to a requester and thus cannot be <br>assigned. See, e.g., RTC Commercial Loan Trust 1995-NP1A v. Winthrop Mgmt., 923 F. Supp. <br>83, 88 (E.D. Va. 1996) (holding that “certain rights are purely personal and cannot be assigned”).</code> | <code>What type of fee waiver is mentioned as being personal to a requester?</code> |
| <code>‘IRO’] staff that reviews Agency records and makes public release determinations with an eye <br>toward evaluating directorate-specific equities.” Id. ¶ 4. Ms. Meeks also explains that “records <br>frequently involve the equities of multiple directorates,” and “[w]hen records implicate the <br>operational interests of multiple directorates, the reviews are conducted by the relevant IROs</code> | <code>Who conducts the reviews when the records implicate the operational interests of multiple directorates?</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`: False
- `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`: 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}
- `tp_size`: 0
- `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 | 91.6964 | - | - | - | - | - |
| 1.0 | 12 | - | 0.6483 | 0.6445 | 0.6004 | 0.5232 | 0.4001 |
| 1.7033 | 20 | 39.6429 | - | - | - | - | - |
| 2.0 | 24 | - | 0.6764 | 0.6716 | 0.6361 | 0.5736 | 0.4374 |
| 2.5275 | 30 | 30.1905 | - | - | - | - | - |
| 3.0 | 36 | - | 0.6768 | 0.6699 | 0.6441 | 0.5869 | 0.4416 |
| 3.3516 | 40 | 26.8879 | - | - | - | - | - |
| **3.7033** | **44** | **-** | **0.6774** | **0.6722** | **0.6454** | **0.5892** | **0.4444** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.0.1
- Transformers: 4.50.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.5.0
- 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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