--- base_model: sentence-transformers/all-MiniLM-L6-v2 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:555 - loss:MultipleNegativesRankingLoss widget: - source_sentence: What does this text say about unclassified? sentences: - "these sources. \nErrors in third-party GAI components can also have downstream\ \ impacts on accuracy and robustness. \nFor example, test datasets commonly used\ \ to benchmark or validate models can contain label errors. \nInaccuracies in\ \ these labels can impact the “stability” or robustness of these benchmarks, which\ \ many \nGAI practitioners consider during the model selection process. \nTrustworthy\ \ AI Characteristics: Accountable and Transparent, Explainable and Interpretable,\ \ Fair with \nHarmful Bias Managed, Privacy Enhanced, Safe, Secure and Resilient,\ \ Valid and Reliable \n3. \nSuggested Actions to Manage GAI Risks \nThe following\ \ suggested actions target risks unique to or exacerbated by GAI. \nIn addition\ \ to the suggested actions below, AI risk management activities and actions set\ \ forth in the AI \nRMF 1.0 and Playbook are already applicable for managing GAI\ \ risks. Organizations are encouraged to" - "and hardware vulnerabilities; labor practices; data privacy and localization\ \ \ncompliance; geopolitical alignment). \nData Privacy; Information Security;\ \ \nValue Chain and Component \nIntegration; Harmful Bias and \nHomogenization\ \ \nMG-3.1-003 \nRe-assess model risks after fine-tuning or retrieval-augmented\ \ generation \nimplementation and for any third-party GAI models deployed for\ \ applications \nand/or use cases that were not evaluated in initial testing.\ \ \nValue Chain and Component \nIntegration \nMG-3.1-004 \nTake reasonable measures\ \ to review training data for CBRN information, and \nintellectual property, and\ \ where appropriate, remove it. Implement reasonable \nmeasures to prevent, flag,\ \ or take other action in response to outputs that \nreproduce particular training\ \ data (e.g., plagiarized, trademarked, patented, \nlicensed content or trade\ \ secret material). \nIntellectual Property; CBRN \nInformation or Capabilities\ \ \n \n43" - "• \nStage of the AI lifecycle: Risks can arise during design, development, deployment,\ \ operation, \nand/or decommissioning. \n• \nScope: Risks may exist at individual\ \ model or system levels, at the application or implementation \nlevels (i.e.,\ \ for a specific use case), or at the ecosystem level – that is, beyond a single\ \ system or \norganizational context. Examples of the latter include the expansion\ \ of “algorithmic \nmonocultures,3” resulting from repeated use of the same model,\ \ or impacts on access to \nopportunity, labor markets, and the creative economies.4\ \ \n• \nSource of risk: Risks may emerge from factors related to the design, training,\ \ or operation of the \nGAI model itself, stemming in some cases from GAI model\ \ or system inputs, and in other cases, \nfrom GAI system outputs. Many GAI risks,\ \ however, originate from human behavior, including \n \n \n3 “Algorithmic monocultures”\ \ refers to the phenomenon in which repeated use of the same model or algorithm\ \ in" - source_sentence: What does this text say about unclassified? sentences: - "Security; Dangerous, Violent, or \nHateful Content \n \n34 \nMS-2.7-009 Regularly\ \ assess and verify that security measures remain effective and have not \nbeen\ \ compromised. \nInformation Security \nAI Actor Tasks: AI Deployment, AI Impact\ \ Assessment, Domain Experts, Operation and Monitoring, TEVV \n \nMEASURE 2.8:\ \ Risks associated with transparency and accountability – as identified in the\ \ MAP function – are examined and \ndocumented. \nAction ID \nSuggested Action\ \ \nGAI Risks \nMS-2.8-001 \nCompile statistics on actual policy violations, take-down\ \ requests, and intellectual \nproperty infringement for organizational GAI systems:\ \ Analyze transparency \nreports across demographic groups, languages groups.\ \ \nIntellectual Property; Harmful Bias \nand Homogenization \nMS-2.8-002 Document\ \ the instructions given to data annotators or AI red-teamers. \nHuman-AI Configuration\ \ \nMS-2.8-003 \nUse digital content transparency solutions to enable the documentation\ \ of each" - "information during GAI training and maintenance. \nHuman-AI Configuration; Obscene,\ \ \nDegrading, and/or Abusive \nContent; Value Chain and \nComponent Integration;\ \ \nDangerous, Violent, or Hateful \nContent \nMS-2.6-002 \nAssess existence or\ \ levels of harmful bias, intellectual property infringement, \ndata privacy violations,\ \ obscenity, extremism, violence, or CBRN information in \nsystem training data.\ \ \nData Privacy; Intellectual Property; \nObscene, Degrading, and/or \nAbusive\ \ Content; Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful\ \ Content; CBRN \nInformation or Capabilities \nMS-2.6-003 Re-evaluate safety\ \ features of fine-tuned models when the negative risk exceeds \norganizational\ \ risk tolerance. \nDangerous, Violent, or Hateful \nContent \nMS-2.6-004 Review\ \ GAI system outputs for validity and safety: Review generated code to \nassess\ \ risks that may arise from unreliable downstream decision-making. \nValue Chain\ \ and Component \nIntegration; Dangerous, Violent, or \nHateful Content" - "Information Integrity; Harmful Bias \nand Homogenization \nAI Actor Tasks: AI\ \ Deployment, AI Impact Assessment, Domain Experts, End-Users, Operation and Monitoring,\ \ TEVV \n \nMEASURE 2.10: Privacy risk of the AI system – as identified in the\ \ MAP function – is examined and documented. \nAction ID \nSuggested Action \n\ GAI Risks \nMS-2.10-001 \nConduct AI red-teaming to assess issues such as: Outputting\ \ of training data \nsamples, and subsequent reverse engineering, model extraction,\ \ and \nmembership inference risks; Revealing biometric, confidential, copyrighted,\ \ \nlicensed, patented, personal, proprietary, sensitive, or trade-marked information;\ \ \nTracking or revealing location information of users or members of training\ \ \ndatasets. \nHuman-AI Configuration; \nInformation Integrity; Intellectual \n\ Property \nMS-2.10-002 \nEngage directly with end-users and other stakeholders\ \ to understand their \nexpectations and concerns regarding content provenance.\ \ Use this feedback to" - source_sentence: What does this text say about risk management? sentences: - "robust watermarking techniques and corresponding detectors to identify the source\ \ of content or \nmetadata recording techniques and metadata management tools\ \ and repositories to trace content \norigins and modifications. Further narrowing\ \ of GAI task definitions to include provenance data can \nenable organizations\ \ to maximize the utility of provenance data and risk management efforts. \nA.1.7.\ \ Enhancing Content Provenance through Structured Public Feedback \nWhile indirect\ \ feedback methods such as automated error collection systems are useful, they\ \ often lack \nthe context and depth that direct input from end users can provide.\ \ Organizations can leverage feedback \napproaches described in the Pre-Deployment\ \ Testing section to capture input from external sources such \nas through AI\ \ red-teaming. \nIntegrating pre- and post-deployment external feedback into\ \ the monitoring process for GAI models and" - "tools for monitoring third-party GAI risks; Consider policy adjustments across\ \ GAI \nmodeling libraries, tools and APIs, fine-tuned models, and embedded tools;\ \ \nAssess GAI vendors, open-source or proprietary GAI tools, or GAI service \n\ providers against incident or vulnerability databases. \nData Privacy; Human-AI\ \ \nConfiguration; Information \nSecurity; Intellectual Property; \nValue Chain\ \ and Component \nIntegration; Harmful Bias and \nHomogenization \nGV-6.1-010\ \ \nUpdate GAI acceptable use policies to address proprietary and open-source\ \ GAI \ntechnologies and data, and contractors, consultants, and other third-party\ \ \npersonnel. \nIntellectual Property; Value Chain \nand Component Integration\ \ \nAI Actor Tasks: Operation and Monitoring, Procurement, Third-party entities\ \ \n \nGOVERN 6.2: Contingency processes are in place to handle failures or incidents\ \ in third-party data or AI systems deemed to be \nhigh-risk. \nAction ID \nSuggested\ \ Action \nGAI Risks \nGV-6.2-001" - "MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively\ \ or quantitatively and demonstrated for \nconditions similar to deployment setting(s).\ \ Measures are documented. \nAction ID \nSuggested Action \nGAI Risks \nMS-2.3-001\ \ Consider baseline model performance on suites of benchmarks when selecting a\ \ \nmodel for fine tuning or enhancement with retrieval-augmented generation. \n\ Information Security; \nConfabulation \nMS-2.3-002 Evaluate claims of model capabilities\ \ using empirically validated methods. \nConfabulation; Information \nSecurity\ \ \nMS-2.3-003 Share results of pre-deployment testing with relevant GAI Actors,\ \ such as those \nwith system release approval authority. \nHuman-AI Configuration\ \ \n \n31 \nMS-2.3-004 \nUtilize a purpose-built testing environment such as NIST\ \ Dioptra to empirically \nevaluate GAI trustworthy characteristics. \nCBRN Information\ \ or Capabilities; \nData Privacy; Confabulation; \nInformation Integrity; Information\ \ \nSecurity; Dangerous, Violent, or" - source_sentence: What does this text say about unclassified? sentences: - "techniques such as re-sampling, re-ranking, or adversarial training to mitigate\ \ \nbiases in the generated content. \nInformation Security; Harmful Bias \nand\ \ Homogenization \nMG-2.2-005 \nEngage in due diligence to analyze GAI output\ \ for harmful content, potential \nmisinformation, and CBRN-related or NCII content.\ \ \nCBRN Information or Capabilities; \nObscene, Degrading, and/or \nAbusive Content;\ \ Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful Content\ \ \n \n41 \nMG-2.2-006 \nUse feedback from internal and external AI Actors, users,\ \ individuals, and \ncommunities, to assess impact of AI-generated content. \n\ Human-AI Configuration \nMG-2.2-007 \nUse real-time auditing tools where they can\ \ be demonstrated to aid in the \ntracking and validation of the lineage and authenticity\ \ of AI-generated data. \nInformation Integrity \nMG-2.2-008 \nUse structured\ \ feedback mechanisms to solicit and capture user input about AI-\ngenerated content\ \ to detect subtle shifts in quality or alignment with" - "Human-AI Configuration; Value \nChain and Component Integration \nMP-5.2-002 \n\ Plan regular engagements with AI Actors responsible for inputs to GAI systems,\ \ \nincluding third-party data and algorithms, to review and evaluate unanticipated\ \ \nimpacts. \nHuman-AI Configuration; Value \nChain and Component Integration\ \ \nAI Actor Tasks: AI Deployment, AI Design, AI Impact Assessment, Affected Individuals\ \ and Communities, Domain Experts, End-\nUsers, Human Factors, Operation and Monitoring\ \ \n \nMEASURE 1.1: Approaches and metrics for measurement of AI risks enumerated\ \ during the MAP function are selected for \nimplementation starting with the\ \ most significant AI risks. The risks or trustworthiness characteristics that\ \ will not – or cannot – be \nmeasured are properly documented. \nAction ID \n\ Suggested Action \nGAI Risks \nMS-1.1-001 Employ methods to trace the origin and\ \ modifications of digital content. \nInformation Integrity \nMS-1.1-002" - "input them directly to a GAI system, with a variety of downstream negative consequences\ \ to \ninterconnected systems. Indirect prompt injection attacks occur when adversaries\ \ remotely (i.e., without \na direct interface) exploit LLM-integrated applications\ \ by injecting prompts into data likely to be \nretrieved. Security researchers\ \ have already demonstrated how indirect prompt injections can exploit \nvulnerabilities\ \ by stealing proprietary data or running malicious code remotely on a machine.\ \ Merely \nquerying a closed production model can elicit previously undisclosed\ \ information about that model. \nAnother cybersecurity risk to GAI is data poisoning,\ \ in which an adversary compromises a training \ndataset used by a model to manipulate\ \ its outputs or operation. Malicious tampering with data or parts \nof the model\ \ could exacerbate risks associated with GAI system outputs. \nTrustworthy AI\ \ Characteristics: Privacy Enhanced, Safe, Secure and Resilient, Valid and Reliable\ \ \n2.10." - source_sentence: What does this text say about data privacy? sentences: - "Property. We also note that some risks are cross-cutting between these categories.\ \ \n \n4 \n1. CBRN Information or Capabilities: Eased access to or synthesis\ \ of materially nefarious \ninformation or design capabilities related to chemical,\ \ biological, radiological, or nuclear (CBRN) \nweapons or other dangerous materials\ \ or agents. \n2. Confabulation: The production of confidently stated but erroneous\ \ or false content (known \ncolloquially as “hallucinations” or “fabrications”)\ \ by which users may be misled or deceived.6 \n3. Dangerous, Violent, or Hateful\ \ Content: Eased production of and access to violent, inciting, \nradicalizing,\ \ or threatening content as well as recommendations to carry out self-harm or\ \ \nconduct illegal activities. Includes difficulty controlling public exposure\ \ to hateful and disparaging \nor stereotyping content. \n4. Data Privacy: Impacts\ \ due to leakage and unauthorized use, disclosure, or de-anonymization of" - "information during GAI training and maintenance. \nHuman-AI Configuration; Obscene,\ \ \nDegrading, and/or Abusive \nContent; Value Chain and \nComponent Integration;\ \ \nDangerous, Violent, or Hateful \nContent \nMS-2.6-002 \nAssess existence or\ \ levels of harmful bias, intellectual property infringement, \ndata privacy violations,\ \ obscenity, extremism, violence, or CBRN information in \nsystem training data.\ \ \nData Privacy; Intellectual Property; \nObscene, Degrading, and/or \nAbusive\ \ Content; Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful\ \ Content; CBRN \nInformation or Capabilities \nMS-2.6-003 Re-evaluate safety\ \ features of fine-tuned models when the negative risk exceeds \norganizational\ \ risk tolerance. \nDangerous, Violent, or Hateful \nContent \nMS-2.6-004 Review\ \ GAI system outputs for validity and safety: Review generated code to \nassess\ \ risks that may arise from unreliable downstream decision-making. \nValue Chain\ \ and Component \nIntegration; Dangerous, Violent, or \nHateful Content" - "Scheurer, J. et al. (2023) Technical report: Large language models can strategically\ \ deceive their users \nwhen put under pressure. arXiv. https://arxiv.org/abs/2311.07590\ \ \nShelby, R. et al. (2023) Sociotechnical Harms of Algorithmic Systems: Scoping\ \ a Taxonomy for Harm \nReduction. arXiv. https://arxiv.org/pdf/2210.05791 \n\ Shevlane, T. et al. (2023) Model evaluation for extreme risks. arXiv. https://arxiv.org/pdf/2305.15324\ \ \nShumailov, I. et al. (2023) The curse of recursion: training on generated\ \ data makes models forget. arXiv. \nhttps://arxiv.org/pdf/2305.17493v2 \nSmith,\ \ A. et al. (2023) Hallucination or Confabulation? Neuroanatomy as metaphor in\ \ Large Language \nModels. PLOS Digital Health. \nhttps://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388\ \ \nSoice, E. et al. (2023) Can large language models democratize access to dual-use\ \ biotechnology? arXiv. \nhttps://arxiv.org/abs/2306.03809" --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id") # Run inference sentences = [ 'What does this text say about data privacy?', 'information during GAI training and maintenance. \nHuman-AI Configuration; Obscene, \nDegrading, and/or Abusive \nContent; Value Chain and \nComponent Integration; \nDangerous, Violent, or Hateful \nContent \nMS-2.6-002 \nAssess existence or levels of harmful bias, intellectual property infringement, \ndata privacy violations, obscenity, extremism, violence, or CBRN information in \nsystem training data. \nData Privacy; Intellectual Property; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful Content; CBRN \nInformation or Capabilities \nMS-2.6-003 Re-evaluate safety features of fine-tuned models when the negative risk exceeds \norganizational risk tolerance. \nDangerous, Violent, or Hateful \nContent \nMS-2.6-004 Review GAI system outputs for validity and safety: Review generated code to \nassess risks that may arise from unreliable downstream decision-making. \nValue Chain and Component \nIntegration; Dangerous, Violent, or \nHateful Content', 'Scheurer, J. et al. (2023) Technical report: Large language models can strategically deceive their users \nwhen put under pressure. arXiv. https://arxiv.org/abs/2311.07590 \nShelby, R. et al. (2023) Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm \nReduction. arXiv. https://arxiv.org/pdf/2210.05791 \nShevlane, T. et al. (2023) Model evaluation for extreme risks. arXiv. https://arxiv.org/pdf/2305.15324 \nShumailov, I. et al. (2023) The curse of recursion: training on generated data makes models forget. arXiv. \nhttps://arxiv.org/pdf/2305.17493v2 \nSmith, A. et al. (2023) Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language \nModels. PLOS Digital Health. \nhttps://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388 \nSoice, E. et al. (2023) Can large language models democratize access to dual-use biotechnology? arXiv. \nhttps://arxiv.org/abs/2306.03809', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 555 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 555 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What does this text say about trustworthiness? | other systems.
Information Integrity; Value Chain
and Component Integration
MP-2.2-002
Observe and analyze how the GAI system interacts with external networks, and
identify any potential for negative externalities, particularly where content
provenance might be compromised.
Information Integrity
AI Actor Tasks: End Users

MAP 2.3: Scientific integrity and TEVV considerations are identified and documented, including those related to experimental
design, data collection and selection (e.g., availability, representativeness, suitability), system trustworthiness, and construct
validation
Action ID
Suggested Action
GAI Risks
MP-2.3-001
Assess the accuracy, quality, reliability, and authenticity of GAI output by
comparing it to a set of known ground truth data and by using a variety of
evaluation methods (e.g., human oversight and automated evaluation, proven
cryptographic techniques, review of content inputs).
Information Integrity

25 | | What does this text say about unclassified? | training and TEVV data; Filtering of hate speech or content in GAI system
training data; Prevalence of GAI-generated data in GAI system training data.
Harmful Bias and Homogenization


15 Winogender Schemas is a sample set of paired sentences which differ only by gender of the pronouns used,
which can be used to evaluate gender bias in natural language processing coreference resolution systems.

37
MS-2.11-005
Assess the proportion of synthetic to non-synthetic training data and verify
training data is not overly homogenous or GAI-produced to mitigate concerns of
model collapse.
Harmful Bias and Homogenization
AI Actor Tasks: AI Deployment, AI Impact Assessment, Affected Individuals and Communities, Domain Experts, End-Users,
Operation and Monitoring, TEVV

MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities – as identified in the MAP
function – are assessed and documented.
Action ID
Suggested Action
GAI Risks | | What does this text say about unclassified? | Padmakumar, V. et al. (2024) Does writing with language models reduce content diversity? ICLR.
https://arxiv.org/pdf/2309.05196
Park, P. et. al. (2024) AI deception: A survey of examples, risks, and potential solutions. Patterns, 5(5).
arXiv. https://arxiv.org/pdf/2308.14752
Partnership on AI (2023) Building a Glossary for Synthetic Media Transparency Methods, Part 1: Indirect
Disclosure. https://partnershiponai.org/glossary-for-synthetic-media-transparency-methods-part-1-
indirect-disclosure/
Qu, Y. et al. (2023) Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-
To-Image Models. arXiv. https://arxiv.org/pdf/2305.13873
Rafat, K. et al. (2023) Mitigating carbon footprint for knowledge distillation based deep learning model
compression. PLOS One. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0285668
Said, I. et al. (2022) Nonconsensual Distribution of Intimate Images: Exploring the Role of Legal Attitudes | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 3 - `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
### Framework Versions - Python: 3.11.5 - Sentence Transformers: 3.1.1 - Transformers: 4.44.2 - PyTorch: 2.4.1+cpu - 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", } ``` #### 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} } ```