Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,764 @@
+---
+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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "nomic-ai/modernbert-embed-base",
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
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+  "classifier_pooling": "mean",
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+  "decoder_bias": true,
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+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "reference_compile": true,
+  "repad_logits_with_grad": false,
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+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.48.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+oid sha256:03b2dd1eebc806725922f178d29623e6284c75e0fbbdee62885e223046196e5d
+size 596070136

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 1024,
+  "do_lower_case": false
+}

special_tokens_map.json

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