--- base_model: sentence-transformers/all-mpnet-base-v2 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 - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:714 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: What does the term 'rights, opportunities, or access' encompass in this framework? sentences: - "10 \nGAI systems can ease the unintentional production or dissemination of false,\ \ inaccurate, or misleading \ncontent (misinformation) at scale, particularly\ \ if the content stems from confabulations. \nGAI systems can also ease the deliberate\ \ production or dissemination of false or misleading information \n(disinformation)\ \ at scale, where an actor has the explicit intent to deceive or cause harm to\ \ others. Even \nvery subtle changes to text or images can manipulate human and\ \ machine perception. \nSimilarly, GAI systems could enable a higher degree of\ \ sophistication for malicious actors to produce \ndisinformation that is targeted\ \ towards specific demographics. Current and emerging multimodal models \nmake\ \ it possible to generate both text-based disinformation and highly realistic\ \ “deepfakes” – that is, \nsynthetic audiovisual content and photorealistic images.12\ \ Additional disinformation threats could be \nenabled by future GAI models trained\ \ on new data modalities." - '74. See, e.g., Heather Morrison. Virtual Testing Puts Disabled Students at a Disadvantage. Government Technology. May 24, 2022. https://www.govtech.com/education/k-12/virtual-testing-puts-disabled-students-at-a-disadvantage; Lydia X. Z. Brown, Ridhi Shetty, Matt Scherer, and Andrew Crawford. Ableism And Disability Discrimination In New Surveillance Technologies: How new surveillance technologies in education, policing, health care, and the workplace disproportionately harm disabled people. Center for Democracy and Technology Report. May 24, 2022. https://cdt.org/insights/ableism-and-disability-discrimination-in-new-surveillance-technologies-how­ new-surveillance-technologies-in-education-policing-health-care-and-the-workplace­ disproportionately-harm-disabled-people/ 69' - "persons, Asian Americans and Pacific Islanders and other persons of color; members\ \ of religious minorities; \nwomen, girls, and non-binary people; lesbian, gay,\ \ bisexual, transgender, queer, and intersex (LGBTQI+) \npersons; older adults;\ \ persons with disabilities; persons who live in rural areas; and persons otherwise\ \ adversely \naffected by persistent poverty or inequality. \nRIGHTS, OPPORTUNITIES,\ \ OR ACCESS: “Rights, opportunities, or access” is used to indicate the scoping\ \ \nof this framework. It describes the set of: civil rights, civil liberties,\ \ and privacy, including freedom of speech, \nvoting, and protections from discrimination,\ \ excessive punishment, unlawful surveillance, and violations of \nprivacy and\ \ other freedoms in both public and private sector contexts; equal opportunities,\ \ including equitable \naccess to education, housing, credit, employment, and\ \ other programs; or, access to critical resources or" - source_sentence: What are some broad negative risks associated with GAI design, development, and deployment? sentences: - "actually occurring, or large-scale risks could occur); and broad GAI negative\ \ risks, \nincluding: Immature safety or risk cultures related to AI and GAI design,\ \ \ndevelopment and deployment, public information integrity risks, including\ \ impacts \non democratic processes, unknown long-term performance characteristics\ \ of GAI. \nInformation Integrity; Dangerous, \nViolent, or Hateful Content; CBRN\ \ \nInformation or Capabilities \nGV-1.3-007 Devise a plan to halt development\ \ or deployment of a GAI system that poses \nunacceptable negative risk. \nCBRN\ \ Information and Capability; \nInformation Security; Information \nIntegrity\ \ \nAI Actor Tasks: Governance and Oversight \n \nGOVERN 1.4: The risk management\ \ process and its outcomes are established through transparent policies, procedures,\ \ and other \ncontrols based on organizational risk priorities. \nAction ID \n\ Suggested Action \nGAI Risks \nGV-1.4-001 \nEstablish policies and mechanisms\ \ to prevent GAI systems from generating" - "39 \nMS-3.3-004 \nProvide input for training materials about the capabilities\ \ and limitations of GAI \nsystems related to digital content transparency for\ \ AI Actors, other \nprofessionals, and the public about the societal impacts\ \ of AI and the role of \ndiverse and inclusive content generation. \nHuman-AI\ \ Configuration; \nInformation Integrity; Harmful Bias \nand Homogenization \n\ MS-3.3-005 \nRecord and integrate structured feedback about content provenance\ \ from \noperators, users, and potentially impacted communities through the use\ \ of \nmethods such as user research studies, focus groups, or community forums.\ \ \nActively seek feedback on generated content quality and potential biases.\ \ \nAssess the general awareness among end users and impacted communities \nabout\ \ the availability of these feedback channels. \nHuman-AI Configuration; \nInformation\ \ Integrity; Harmful Bias \nand Homogenization \nAI Actor Tasks: AI Deployment,\ \ Affected Individuals and Communities, End-Users, Operation and Monitoring, TEVV" - "NOTICE & \nEXPLANATION \nWHY THIS PRINCIPLE IS IMPORTANT\nThis section provides\ \ a brief summary of the problems which the principle seeks to address and protect\ \ \nagainst, including illustrative examples. \nAutomated systems now determine\ \ opportunities, from employment to credit, and directly shape the American \n\ public’s experiences, from the courtroom to online classrooms, in ways that profoundly\ \ impact people’s lives. But this \nexpansive impact is not always visible. An\ \ applicant might not know whether a person rejected their resume or a \nhiring\ \ algorithm moved them to the bottom of the list. A defendant in the courtroom\ \ might not know if a judge deny­\ning their bail is informed by an automated\ \ system that labeled them “high risk.” From correcting errors to contesting \n\ decisions, people are often denied the knowledge they need to address the impact\ \ of automated systems on their lives." - source_sentence: Who should conduct the assessment of the impact of surveillance on rights and opportunities? sentences: - "APPENDIX\n•\nJulia Simon-Mishel, Supervising Attorney, Philadelphia Legal Assistance\n\ •\nDr. Zachary Mahafza, Research & Data Analyst, Southern Poverty Law Center\n\ •\nJ. Khadijah Abdurahman, Tech Impact Network Research Fellow, AI Now Institute,\ \ UCLA C2I1, and\nUWA Law School\nPanelists separately described the increasing\ \ scope of technology use in providing for social welfare, including \nin fraud\ \ detection, digital ID systems, and other methods focused on improving efficiency\ \ and reducing cost. \nHowever, various panelists individually cautioned that\ \ these systems may reduce burden for government \nagencies by increasing the\ \ burden and agency of people using and interacting with these technologies. \n\ Additionally, these systems can produce feedback loops and compounded harm, collecting\ \ data from \ncommunities and using it to reinforce inequality. Various panelists\ \ suggested that these harms could be" - "assessments, including data retention timelines and associated justification,\ \ and an assessment of the \nimpact of surveillance or data collection on rights,\ \ opportunities, and access. Where possible, this \nassessment of the impact of\ \ surveillance should be done by an independent party. Reporting should be \n\ provided in a clear and machine-readable manner. \n35" - "access to education, housing, credit, employment, and other programs; or, access\ \ to critical resources or \nservices, such as healthcare, financial services,\ \ safety, social services, non-deceptive information about goods \nand services,\ \ and government benefits. \n10" - source_sentence: How can voting-related systems impact privacy and security? sentences: - "as custody and divorce information, and home, work, or school environmental data);\ \ or have the reasonable potential \nto be used in ways that are likely to expose\ \ individuals to meaningful harm, such as a loss of privacy or financial harm\ \ \ndue to identity theft. Data and metadata generated by or about those who are\ \ not yet legal adults is also sensitive, even \nif not related to a sensitive\ \ domain. Such data includes, but is not limited to, numerical, text, image, audio,\ \ or video \ndata. “Sensitive domains” are those in which activities being conducted\ \ can cause material harms, including signifi­\ncant adverse effects on human\ \ rights such as autonomy and dignity, as well as civil liberties and civil rights.\ \ Domains \nthat have historically been singled out as deserving of enhanced data\ \ protections or where such enhanced protections \nare reasonably expected by\ \ the public include, but are not limited to, health, family planning and care,\ \ employment," - "agreed upon the importance of advisory boards and compensated community input\ \ early in the design process \n(before the technology is built and instituted).\ \ Various panelists also emphasized the importance of regulation \nthat includes\ \ limits to the type and cost of such technologies. \n56" - "Surveillance and criminal justice system algorithms such as risk assessments,\ \ predictive \n policing, automated license plate readers, real-time facial\ \ recognition systems (especially \n those used in public places or during\ \ protected activities like peaceful protests), social media \n monitoring,\ \ and ankle monitoring devices; \nVoting-related systems such as signature matching\ \ tools; \nSystems with a potential privacy impact such as smart home systems\ \ and associated data, \n systems that use or collect health-related data,\ \ systems that use or collect education-related \n data, criminal justice\ \ system data, ad-targeting systems, and systems that perform big data \n \ \ analytics in order to build profiles or infer personal information about individuals;\ \ and \nAny system that has the meaningful potential to lead to algorithmic discrimination.\ \ \n• Equal opportunities, including but not limited to:" - source_sentence: What impact do automated systems have on underserved communities? sentences: - "generation, summarization, search, and chat. These activities can take place\ \ within organizational \nsettings or in the public domain. \nOrganizations can\ \ restrict AI applications that cause harm, exceed stated risk tolerances, or\ \ that conflict \nwith their tolerances or values. Governance tools and protocols\ \ that are applied to other types of AI \nsystems can be applied to GAI systems.\ \ These plans and actions include: \n• Accessibility and reasonable \naccommodations\ \ \n• AI actor credentials and qualifications \n• Alignment to organizational\ \ values \n• Auditing and assessment \n• Change-management controls \n• Commercial\ \ use \n• Data provenance" - "automated systems make on underserved communities and to institute proactive\ \ protections that support these \ncommunities. \n•\nAn automated system using\ \ nontraditional factors such as educational attainment and employment history\ \ as\npart of its loan underwriting and pricing model was found to be much more\ \ likely to charge an applicant who\nattended a Historically Black College or\ \ University (HBCU) higher loan prices for refinancing a student loan\nthan an\ \ applicant who did not attend an HBCU. This was found to be true even when controlling\ \ for\nother credit-related factors.32\n•\nA hiring tool that learned the features\ \ of a company's employees (predominantly men) rejected women appli­\ncants for\ \ spurious and discriminatory reasons; resumes with the word “women’s,” such as\ \ “women’s\nchess club captain,” were penalized in the candidate ranking.33\n\ •\nA predictive model marketed as being able to predict whether students are likely\ \ to drop out of school was" - "on a principle of local control, such that those individuals closest to the data\ \ subject have more access while \nthose who are less proximate do not (e.g.,\ \ a teacher has access to their students’ daily progress data while a \nsuperintendent\ \ does not). \nReporting. In addition to the reporting on data privacy (as listed\ \ above for non-sensitive data), entities devel-\noping technologies related to\ \ a sensitive domain and those collecting, using, storing, or sharing sensitive\ \ data \nshould, whenever appropriate, regularly provide public reports describing:\ \ any data security lapses or breaches \nthat resulted in sensitive data leaks;\ \ the number, type, and outcomes of ethical pre-reviews undertaken; a \ndescription\ \ of any data sold, shared, or made public, and how that data was assessed to\ \ determine it did not pres-\nent a sensitive data risk; and ongoing risk identification\ \ and management procedures, and any mitigation added" model-index: - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.8881578947368421 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.993421052631579 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.993421052631579 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8881578947368421 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.331140350877193 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19868421052631577 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8881578947368421 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.993421052631579 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.993421052631579 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9550417755482483 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9395363408521302 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9395363408521302 name: Cosine Map@100 - type: dot_accuracy@1 value: 0.8881578947368421 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.993421052631579 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.993421052631579 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.8881578947368421 name: Dot Precision@1 - type: dot_precision@3 value: 0.331140350877193 name: Dot Precision@3 - type: dot_precision@5 value: 0.19868421052631577 name: Dot Precision@5 - type: dot_precision@10 value: 0.09999999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.8881578947368421 name: Dot Recall@1 - type: dot_recall@3 value: 0.993421052631579 name: Dot Recall@3 - type: dot_recall@5 value: 0.993421052631579 name: Dot Recall@5 - type: dot_recall@10 value: 1.0 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9550417755482483 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9395363408521302 name: Dot Mrr@10 - type: dot_map@100 value: 0.9395363408521302 name: Dot Map@100 --- # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity ### 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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("jet-taekyo/mpnet_finetuned_recursive") # Run inference sentences = [ 'What impact do automated systems have on underserved communities?', "automated systems make on underserved communities and to institute proactive protections that support these \ncommunities. \n•\nAn automated system using nontraditional factors such as educational attainment and employment history as\npart of its loan underwriting and pricing model was found to be much more likely to charge an applicant who\nattended a Historically Black College or University (HBCU) higher loan prices for refinancing a student loan\nthan an applicant who did not attend an HBCU. This was found to be true even when controlling for\nother credit-related factors.32\n•\nA hiring tool that learned the features of a company's employees (predominantly men) rejected women appli\xad\ncants for spurious and discriminatory reasons; resumes with the word “women’s,” such as “women’s\nchess club captain,” were penalized in the candidate ranking.33\n•\nA predictive model marketed as being able to predict whether students are likely to drop out of school was", 'on a principle of local control, such that those individuals closest to the data subject have more access while \nthose who are less proximate do not (e.g., a teacher has access to their students’ daily progress data while a \nsuperintendent does not). \nReporting. In addition to the reporting on data privacy (as listed above for non-sensitive data), entities devel-\noping technologies related to a sensitive domain and those collecting, using, storing, or sharing sensitive data \nshould, whenever appropriate, regularly provide public reports describing: any data security lapses or breaches \nthat resulted in sensitive data leaks; the number, type, and outcomes of ethical pre-reviews undertaken; a \ndescription of any data sold, shared, or made public, and how that data was assessed to determine it did not pres-\nent a sensitive data risk; and ongoing risk identification and management procedures, and any mitigation added', ] 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 * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8882 | | cosine_accuracy@3 | 0.9934 | | cosine_accuracy@5 | 0.9934 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.8882 | | cosine_precision@3 | 0.3311 | | cosine_precision@5 | 0.1987 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.8882 | | cosine_recall@3 | 0.9934 | | cosine_recall@5 | 0.9934 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.955 | | cosine_mrr@10 | 0.9395 | | **cosine_map@100** | **0.9395** | | dot_accuracy@1 | 0.8882 | | dot_accuracy@3 | 0.9934 | | dot_accuracy@5 | 0.9934 | | dot_accuracy@10 | 1.0 | | dot_precision@1 | 0.8882 | | dot_precision@3 | 0.3311 | | dot_precision@5 | 0.1987 | | dot_precision@10 | 0.1 | | dot_recall@1 | 0.8882 | | dot_recall@3 | 0.9934 | | dot_recall@5 | 0.9934 | | dot_recall@10 | 1.0 | | dot_ndcg@10 | 0.955 | | dot_mrr@10 | 0.9395 | | dot_map@100 | 0.9395 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 714 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 714 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:---------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What information should designers and developers provide about automated systems to ensure transparency? | You should know that an automated system is being used,
and understand how and why it contributes to outcomes
that impact you. Designers, developers, and deployers of automat­
ed systems should provide generally accessible plain language docu­
mentation including clear descriptions of the overall system func­
tioning and the role automation plays, notice that such systems are in
use, the individual or organization responsible for the system, and ex­
planations of outcomes that are clear, timely, and accessible. Such
notice should be kept up-to-date and people impacted by the system
should be notified of significant use case or key functionality chang­
es. You should know how and why an outcome impacting you was de­
termined by an automated system, including when the automated
system is not the sole input determining the outcome. Automated
systems should provide explanations that are technically valid,
meaningful and useful to you and to any operators or others who | | Why is it important for individuals impacted by automated systems to be notified of significant changes in functionality? | You should know that an automated system is being used,
and understand how and why it contributes to outcomes
that impact you. Designers, developers, and deployers of automat­
ed systems should provide generally accessible plain language docu­
mentation including clear descriptions of the overall system func­
tioning and the role automation plays, notice that such systems are in
use, the individual or organization responsible for the system, and ex­
planations of outcomes that are clear, timely, and accessible. Such
notice should be kept up-to-date and people impacted by the system
should be notified of significant use case or key functionality chang­
es. You should know how and why an outcome impacting you was de­
termined by an automated system, including when the automated
system is not the sole input determining the outcome. Automated
systems should provide explanations that are technically valid,
meaningful and useful to you and to any operators or others who | | What specific technical questions does the questionnaire for evaluating software workers cover? | questionnaire that businesses can use proactively when procuring software to evaluate workers. It covers
specific technical questions such as the training data used, model training process, biases identified, and
mitigation steps employed.55
Standards organizations have developed guidelines to incorporate accessibility criteria
into technology design processes. The most prevalent in the United States is the Access Board’s Section
508 regulations,56 which are the technical standards for federal information communication technology (software,
hardware, and web). Other standards include those issued by the International Organization for
Standardization,57 and the World Wide Web Consortium Web Content Accessibility Guidelines,58 a globally
recognized voluntary consensus standard for web content and other information and communications
technology.
NIST has released Special Publication 1270, Towards a Standard for Identifying and Managing Bias | * 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`: steps - `per_device_train_batch_size`: 20 - `per_device_eval_batch_size`: 20 - `num_train_epochs`: 5 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 20 - `per_device_eval_batch_size`: 20 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `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`: False - `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 - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `eval_use_gather_object`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | cosine_map@100 | |:-----:|:----:|:--------------:| | 1.0 | 36 | 0.9395 | ### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.1.0 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - Datasets: 3.0.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```