--- 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) - **Maximum Sequence Length:** 1024 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **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] ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` * Evaluated with [InformationRetrievalEvaluator](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 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,440 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What must the advisory committee make available for public inspection? | advisory committee shall be available for public inspection and copying . . . until the advisory
committee ceases to exist.” Id. § 10(b). Unlike FOIA, this provision looks forward. It requires
committees to take affirmative steps to make their records are public, even absent a request.
FACA’s definition of “advisory committee” has four parts. First, it includes “any | | What did the landlords fail to alert the court about? | court documents containing fake citations, we conclude that
imposing monetary sanctions or dismissing this appeal would be
disproportionate to Al-Hamim’s violation of the Appellate Rules.

23
Further, in their answer brief, the landlords failed to alert this court
to the hallucinations in Al-Hamim’s opening brief and did not
request an award of attorney fees against Al-Hamim. Under the | | On what date was the motion served on the plaintiff’s counsel? | also alleged (1) that plaintiff violated section 2-401(e) and (2) that she lacked good cause to
file anonymously because she signed an affidavit in her own name in another case with similar
allegations. The April 13 motion contains a “Certificate of Service” stating that it was served
on plaintiff’s counsel by e-mail on April 13. | * Loss: [MatryoshkaLoss](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
Click to expand - `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
### 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} } ```