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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:5822 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: nomic-ai/modernbert-embed-base |
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widget: |
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- source_sentence: >- |
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this information about the two documents withheld in part under the |
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deliberative-process |
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128 |
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privilege in No. 11-444, see First Lutz Decl. Ex. DD at 17, 141, but the |
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descriptions of the |
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decisionmaking authority are generic, stating that the withheld information |
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is a “recommendation |
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from the [FOIA] analyst to his/her supervisor,” id. at 17, and a |
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“recommendation from the |
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sentences: |
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- What did the plaintiff assert about the CIA's inaccurate representations? |
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- >- |
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What type of document is mentioned as an exhibit in conjunction with the |
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withheld documents? |
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- >- |
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¿Qué ámbito jurisdiccional es mencionado en el contexto de derechos sobre la |
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propia imagen? |
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- source_sentence: |- |
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Artificial Intelligence, Corp. y que prestaba servicios mediante |
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contrato para el Departamento de Producción de tal corporación. |
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Adujo que, no se encontraba en la obligación de solicitar |
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autorización a la parte apelada para utilizar su imagen, ya que se le |
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había pagado por la producción de múltiples videos publicitarios |
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para el uso de las empresas. |
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Luego |
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de |
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varias |
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incidencias |
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sentences: |
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- Who has the burden to provide a sufficient record on appeal? |
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- >- |
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¿Para qué departamento prestaba servicios Artificial Intelligence, Corp. |
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según el contrato? |
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- What section numbers are referenced for further information? |
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- source_sentence: >- |
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submission by protégé firms. SHS MJAR at 28–30; VCH MJAR at 28–30 (same). |
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This, Plaintiffs |
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contend, violates Section 125.8(e) because it purportedly subjects protégés |
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to heightened |
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evaluation criteria as compared to offerors generally and makes it harder |
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for mentor-protégé JVs |
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to compete against more experienced firms with larger portfolios of past |
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work. SHS MJAR at 28– |
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sentences: |
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- >- |
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On what date were the plaintiff's petition, complaint, and trial court's |
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order filed? |
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- What section do Plaintiffs contend is violated? |
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- What is the amount of pages the party seeks to withhold? |
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- source_sentence: >- |
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Beginning with the CIA’s submissions, the CIA states in its declaration |
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submitted in No. |
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11-445 that “[s]ome of the records for which information has been withheld |
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pursuant to |
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Exemption (b)(5) contain confidential communications between CIA staff and |
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attorneys within |
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the CIA’s Office of General Counsel about the processing of certain FOIA |
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requests.” Third Lutz |
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sentences: |
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- >- |
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What is the subject of the confidential communications mentioned in the |
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document? |
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- >- |
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Which rule number is associated with the responsibilities regarding |
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nonlawyer assistants? |
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- ¿Qué número de referencia tiene el documento? |
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- source_sentence: >- |
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contracting/contracting-assistance-programs/sba-mentor-protege-program (last |
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visited Apr. 19, |
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2023). |
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5 |
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|
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protégé must demonstrate that the added mentor-protégé relationship will not |
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adversely affect the |
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|
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development of either protégé firm (e.g., the second firm may not be a |
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competitor of the first |
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|
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firm).” 13 C.F.R. § 125.9(b)(3). |
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sentences: |
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- >- |
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What discretion do district courts have regarding a defendant’s invocation |
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of FOIA exemptions? |
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- What must the protégé demonstrate about the mentor-protégé relationship? |
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- Which exemptions are mentioned in relation to the plaintiff's accusations? |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: ModernBERT Embed base Legal Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5285935085007728 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5718701700154559 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.6646058732612056 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.7310664605873262 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5285935085007728 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.5141679546625451 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.3941267387944359 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.2329211746522411 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.17877382792375063 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.4894384337970118 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.6120556414219475 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.7184441009788768 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.6300476733345887 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.5741100561811532 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.6186392686743281 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5162287480680062 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5486862442040186 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.6414219474497682 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.7171561051004637 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5162287480680062 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.4981968057702215 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.38083462132921175 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.22720247295208656 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.17400824317362185 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.47346728490468826 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.5910613086038125 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.702344152498712 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.6137901932050573 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.5592913569343243 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.6021884440021203 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.482225656877898 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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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 |
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value: 0.16267387944358577 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.4492529623905203 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
|
value: 0.5569294178258629 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
|
value: 0.6642194744976816 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
|
value: 0.5781404945062661 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.5249122936139936 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.5698418441661705 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.41576506955177744 |
|
name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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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 |
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dataset: |
|
name: dim 64 |
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type: dim_64 |
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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: |
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- AdamLucek/legal-rag-positives-synthetic |
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--- |
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|
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# ModernBERT Embed base Legal Matryoshka |
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|
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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 |
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|
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### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision 92168cbee600b1abbfc10842aba988aa69572291 --> |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
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- [AdamLucek/legal-rag-positives-synthetic](https://huggingface.co/datasets/AdamLucek/legal-rag-positives-synthetic) |
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- **Language:** en |
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- **License:** apache-2.0 |
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|
|
### Full Model Architecture |
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|
|
``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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|
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## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
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|
|
First install the Sentence Transformers library: |
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|
|
```bash |
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pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
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from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("AdamLucek/ModernBERT-embed-base-legal-MRL") |
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# Run inference |
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sentences = [ |
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'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).', |
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'What must the protégé demonstrate about the mentor-protégé relationship?', |
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'What discretion do district courts have regarding a defendant’s invocation of FOIA exemptions?', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
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``` |
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|
|
<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
|
### Metrics |
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#### Information Retrieval |
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|
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* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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|
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| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
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|:--------------------|:---------|:-----------|:-----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.5286 | 0.5162 | 0.4822 | 0.4158 | 0.3122 | |
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| cosine_accuracy@3 | 0.5719 | 0.5487 | 0.5286 | 0.4436 | 0.3509 | |
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| cosine_accuracy@5 | 0.6646 | 0.6414 | 0.5981 | 0.5363 | 0.4359 | |
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| cosine_accuracy@10 | 0.7311 | 0.7172 | 0.6785 | 0.6105 | 0.4791 | |
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| cosine_precision@1 | 0.5286 | 0.5162 | 0.4822 | 0.4158 | 0.3122 | |
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| cosine_precision@3 | 0.5142 | 0.4982 | 0.4699 | 0.3993 | 0.3091 | |
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| cosine_precision@5 | 0.3941 | 0.3808 | 0.3586 | 0.3128 | 0.2504 | |
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| cosine_precision@10 | 0.2329 | 0.2272 | 0.2147 | 0.1924 | 0.1498 | |
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| cosine_recall@1 | 0.1788 | 0.174 | 0.1627 | 0.1426 | 0.105 | |
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| cosine_recall@3 | 0.4894 | 0.4735 | 0.4493 | 0.3836 | 0.2955 | |
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| cosine_recall@5 | 0.6121 | 0.5911 | 0.5569 | 0.4878 | 0.3931 | |
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| cosine_recall@10 | 0.7184 | 0.7023 | 0.6642 | 0.5963 | 0.4681 | |
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| **cosine_ndcg@10** | **0.63** | **0.6138** | **0.5781** | **0.5109** | **0.3956** | |
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| cosine_mrr@10 | 0.5741 | 0.5593 | 0.5249 | 0.4573 | 0.3509 | |
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| cosine_map@100 | 0.6186 | 0.6022 | 0.5698 | 0.503 | 0.3939 | |
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<!-- |
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## Bias, Risks and Limitations |
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*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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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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|
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#### [AdamLucek/legal-rag-positives-synthetic](https://huggingface.co/datasets/AdamLucek/legal-rag-positives-synthetic) |
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|
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* Dataset: [AdamLucek/legal-rag-positives-synthetic](https://huggingface.co/datasets/AdamLucek/legal-rag-positives-synthetic) |
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* Size: 5,822 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | 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} |
|
} |
|
``` |
