Text Classification
Transformers
Safetensors
English
sentinel_stage_a
feature-extraction
custom
compliance
finance
risk-detection
sentinel-stage-a
limited-functionality
model-version:sentinel-mb-c-d11-20260424
custom_code
Instructions to use AurelexAI/sentinel-01-pub with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AurelexAI/sentinel-01-pub with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AurelexAI/sentinel-01-pub", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AurelexAI/sentinel-01-pub", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "checkpoint_format_version": 1, | |
| "created_at": "2026-04-24T13:59:13", | |
| "model_key": "sentinel-mb-c-d11", | |
| "encoder_model": "answerdotai/ModernBERT-base", | |
| "encoder_params_millions": 149.7, | |
| "head_type": "columnar", | |
| "head_code": "c", | |
| "head_variant": "d11", | |
| "head_dropout": 0.1, | |
| "head_div": 1, | |
| "head_mul": 1, | |
| "head_skip": true, | |
| "head_architecture": "funnel", | |
| "model_family": "modernbert-base", | |
| "projection_size": 640, | |
| "trainable_head_params": 14325653, | |
| "artifact_format": "transformers_end_to_end", | |
| "end_to_end_serialized": true, | |
| "dataset_signature": { | |
| "generator_version": "2026-04-07-final-audit-clear-v1", | |
| "counts": { | |
| "train": 900, | |
| "dev": 150, | |
| "test": 150 | |
| }, | |
| "distribution": { | |
| "train": { | |
| "risky": 603, | |
| "clean": 297 | |
| }, | |
| "dev": { | |
| "risky": 142, | |
| "clean": 8 | |
| }, | |
| "test": { | |
| "risky": 142, | |
| "clean": 8 | |
| } | |
| } | |
| }, | |
| "output_signature": { | |
| "violation": { | |
| "type": "binary" | |
| }, | |
| "severity": { | |
| "type": "multiclass", | |
| "labels": [ | |
| "sev_0_compliant_or_ok", | |
| "sev_1_minor", | |
| "sev_2_moderate", | |
| "sev_3_high" | |
| ] | |
| }, | |
| "domain": { | |
| "type": "multiclass", | |
| "labels": [ | |
| "performance_claims_forecasting", | |
| "investment_advice_suitability", | |
| "conflicts_inducements", | |
| "marketing_solicitation_advertising", | |
| "selective_disclosure_fair_access", | |
| "mnpi_insider_trading", | |
| "recordkeeping_supervision", | |
| "ai_automation_capability_claims", | |
| "privacy_confidentiality", | |
| "cybersecurity_internal_controls", | |
| "employment_favoritism_role_conflict", | |
| "aml_and_suspicious_activity", | |
| "other_unknown" | |
| ] | |
| }, | |
| "subtype": { | |
| "type": "multiclass", | |
| "labels": [ | |
| "speculative_outcomes_unqualified", | |
| "implicit_or_explicit_guarantee", | |
| "risk_context_omitted_or_unbalanced", | |
| "unregistered_personalized_investment_advice", | |
| "undisclosed_economic_conflict_or_referral", | |
| "pressure_or_coercion", | |
| "selective_disclosure", | |
| "mnpi_misuse_or_encouragement", | |
| "recordkeeping_or_preapproval_evasion", | |
| "ai_autonomy_or_safety_overstatement", | |
| "credentials_validation_or_compliance_misrepresentation", | |
| "confidential_data_leakage", | |
| "internal_controls_or_exception_process_leakage", | |
| "academic_commercial_role_blurring_or_quid_pro_quo", | |
| "improper_solicitation_offering_pressure", | |
| "excessive_trading_or_account_churning", | |
| "product_switching_without_cost_benefit_analysis", | |
| "dual_registrant_capacity_or_wrap_fee_conflict_confusion", | |
| "elder_exploitation_or_vulnerable_client_signal", | |
| "suspicious_activity_indicator_or_structuring", | |
| "influencer_or_social_media_promotion_compliance_failure", | |
| "crypto_asset_misrepresentation_or_inadequate_disclosure", | |
| "other_unknown" | |
| ] | |
| }, | |
| "jurisdiction": { | |
| "type": "multiclass", | |
| "labels": [ | |
| "US", | |
| "EU", | |
| "UK", | |
| "Other", | |
| "Unknown" | |
| ] | |
| }, | |
| "why": { | |
| "type": "multilabel", | |
| "labels": [ | |
| "forward_looking_statement_unqualified", | |
| "guarantee_or_assurance_language", | |
| "omits_material_risk_or_downside", | |
| "implies_downside_protection_or_no_drawdown", | |
| "cherry_picks_performance_period", | |
| "omits_performance_methodology_or_gross_net_context", | |
| "personalized_trade_or_allocation_recommendation", | |
| "timing_or_sizing_guidance", | |
| "creates_implied_advisory_relationship", | |
| "conflict_not_disclosed", | |
| "referral_relationship_not_disclosed", | |
| "omits_fees_costs_or_reasonably_available_alternatives", | |
| "selective_private_performance_or_fundraising_update", | |
| "off_the_record_or_not_in_writing_language", | |
| "mnpi_possession_indicated", | |
| "encourages_action_before_public_release", | |
| "avoid_recordkeeping_channel_shift", | |
| "bypasses_required_preapproval", | |
| "pressure_scarcity_urgency", | |
| "unsubstantiated_social_proof_or_validation", | |
| "omits_testimonial_endorsement_or_rating_disclosure", | |
| "obscures_required_disclosure_or_form_crs", | |
| "minimizes_need_for_diligence_or_compliance", | |
| "overstates_ai_capability_or_removes_human_oversight", | |
| "claims_compliance_risk_eliminated", | |
| "shares_sensitive_personal_or_financial_data", | |
| "violates_need_to_know_data_minimization", | |
| "shares_sensitive_internal_controls_or_exceptions", | |
| "role_power_imbalance_or_favoritism", | |
| "excessive_trading_cost_to_equity", | |
| "inadequate_customer_profile_or_suitability_basis", | |
| "exploits_vulnerable_or_elderly_client", | |
| "aml_suspicious_activity_indicator", | |
| "omits_switching_costs_and_product_comparison", | |
| "conflict_language_understates_actual_relationship", | |
| "omits_influencer_compensation_or_affiliation_disclosure", | |
| "misrepresents_sipc_or_regulatory_protection_for_crypto", | |
| "data_breach_notification_obligation_triggered", | |
| "impedes_regulatory_reporting_or_whistleblower_rights" | |
| ] | |
| }, | |
| "impacted_principles": { | |
| "type": "multilabel", | |
| "labels": [ | |
| "truthful_non_misleading_communications", | |
| "balanced_risk_reward_presentation", | |
| "no_performance_guarantees_or_promissory_language", | |
| "registration_and_scope_of_advice", | |
| "duty_of_loyalty_conflict_disclosure", | |
| "fair_access_to_material_information", | |
| "insider_trading_and_mnpi_controls", | |
| "supervision_and_books_records", | |
| "privacy_confidentiality_and_secure_handling", | |
| "security_control_integrity", | |
| "role_separation_and_fair_access_in_academia", | |
| "non_coercion_and_no_undue_influence", | |
| "accurate_ai_capability_and_human_oversight", | |
| "client_vulnerability_and_exploitation_prevention", | |
| "aml_and_sanctions_compliance" | |
| ] | |
| }, | |
| "remediation_actions": { | |
| "type": "multilabel", | |
| "labels": [ | |
| "add_forward_looking_disclaimer", | |
| "reframe_as_scenarios_not_expectations", | |
| "add_balanced_risk_and_downside_section", | |
| "remove_or_soften_guarantee_language", | |
| "remove_personalized_recommendations", | |
| "add_registered_advice_boundary_language", | |
| "disclose_conflicts_and_compensation", | |
| "add_fees_costs_and_alternatives_comparison", | |
| "use_standardized_approved_performance_materials", | |
| "add_performance_methodology_and_gross_net_context", | |
| "avoid_selective_disclosure_share_broadly", | |
| "escalate_mnpi_to_compliance_and_halt", | |
| "keep_discussion_on_retained_channels", | |
| "require_formal_preapproval_before_send", | |
| "remove_pressure_scarcity_and_use_factual_timeline", | |
| "substantiation_or_remove_credibility_claims", | |
| "add_testimonial_endorsement_and_rating_disclosure", | |
| "make_required_disclosure_clear_and_prominent", | |
| "avoid_minimizing_compliance_or_diligence", | |
| "clarify_ai_is_assistive_with_human_review", | |
| "remove_claims_that_ai_eliminates_risk", | |
| "redact_and_minimize_sensitive_data", | |
| "use_secure_transfer_and_limit_access", | |
| "avoid_sharing_internal_controls_or_sanitize", | |
| "route_academic_opportunities_through_institution", | |
| "separate_recommendation_letters_from_work", | |
| "assess_cost_to_equity_against_client_profile", | |
| "flag_for_elder_exploitation_review_and_hold", | |
| "assess_sar_filing_obligation_and_escalate", | |
| "initiate_breach_notification_review_and_timeline", | |
| "remove_provisions_impeding_regulatory_communications" | |
| ] | |
| }, | |
| "content_type": { | |
| "type": "multiclass", | |
| "labels": [ | |
| "email", | |
| "message" | |
| ] | |
| }, | |
| "audience_segment": { | |
| "type": "multiclass", | |
| "labels": [ | |
| "client", | |
| "internal", | |
| "prospect_or_investor", | |
| "public", | |
| "third_party" | |
| ] | |
| }, | |
| "detection_difficulty": { | |
| "type": "multiclass", | |
| "labels": [ | |
| "obvious", | |
| "moderate", | |
| "subtle" | |
| ] | |
| }, | |
| "aggravating_factors": { | |
| "type": "multilabel", | |
| "labels": [ | |
| "intentional", | |
| "reckless", | |
| "negligent", | |
| "concealment_present", | |
| "customer_harm_potential", | |
| "financial_benefit_to_respondent", | |
| "vulnerable_client", | |
| "pattern_or_duration" | |
| ] | |
| } | |
| }, | |
| "label_groups": { | |
| "severity": [ | |
| "sev_0_compliant_or_ok", | |
| "sev_1_minor", | |
| "sev_2_moderate", | |
| "sev_3_high" | |
| ], | |
| "domain": [ | |
| "performance_claims_forecasting", | |
| "investment_advice_suitability", | |
| "conflicts_inducements", | |
| "marketing_solicitation_advertising", | |
| "selective_disclosure_fair_access", | |
| "mnpi_insider_trading", | |
| "recordkeeping_supervision", | |
| "ai_automation_capability_claims", | |
| "privacy_confidentiality", | |
| "cybersecurity_internal_controls", | |
| "employment_favoritism_role_conflict", | |
| "aml_and_suspicious_activity", | |
| "other_unknown" | |
| ], | |
| "subtype": [ | |
| "speculative_outcomes_unqualified", | |
| "implicit_or_explicit_guarantee", | |
| "risk_context_omitted_or_unbalanced", | |
| "unregistered_personalized_investment_advice", | |
| "undisclosed_economic_conflict_or_referral", | |
| "pressure_or_coercion", | |
| "selective_disclosure", | |
| "mnpi_misuse_or_encouragement", | |
| "recordkeeping_or_preapproval_evasion", | |
| "ai_autonomy_or_safety_overstatement", | |
| "credentials_validation_or_compliance_misrepresentation", | |
| "confidential_data_leakage", | |
| "internal_controls_or_exception_process_leakage", | |
| "academic_commercial_role_blurring_or_quid_pro_quo", | |
| "improper_solicitation_offering_pressure", | |
| "excessive_trading_or_account_churning", | |
| "product_switching_without_cost_benefit_analysis", | |
| "dual_registrant_capacity_or_wrap_fee_conflict_confusion", | |
| "elder_exploitation_or_vulnerable_client_signal", | |
| "suspicious_activity_indicator_or_structuring", | |
| "influencer_or_social_media_promotion_compliance_failure", | |
| "crypto_asset_misrepresentation_or_inadequate_disclosure", | |
| "other_unknown" | |
| ], | |
| "jurisdiction": [ | |
| "US", | |
| "EU", | |
| "UK", | |
| "Other", | |
| "Unknown" | |
| ], | |
| "why": [ | |
| "forward_looking_statement_unqualified", | |
| "guarantee_or_assurance_language", | |
| "omits_material_risk_or_downside", | |
| "implies_downside_protection_or_no_drawdown", | |
| "cherry_picks_performance_period", | |
| "omits_performance_methodology_or_gross_net_context", | |
| "personalized_trade_or_allocation_recommendation", | |
| "timing_or_sizing_guidance", | |
| "creates_implied_advisory_relationship", | |
| "conflict_not_disclosed", | |
| "referral_relationship_not_disclosed", | |
| "omits_fees_costs_or_reasonably_available_alternatives", | |
| "selective_private_performance_or_fundraising_update", | |
| "off_the_record_or_not_in_writing_language", | |
| "mnpi_possession_indicated", | |
| "encourages_action_before_public_release", | |
| "avoid_recordkeeping_channel_shift", | |
| "bypasses_required_preapproval", | |
| "pressure_scarcity_urgency", | |
| "unsubstantiated_social_proof_or_validation", | |
| "omits_testimonial_endorsement_or_rating_disclosure", | |
| "obscures_required_disclosure_or_form_crs", | |
| "minimizes_need_for_diligence_or_compliance", | |
| "overstates_ai_capability_or_removes_human_oversight", | |
| "claims_compliance_risk_eliminated", | |
| "shares_sensitive_personal_or_financial_data", | |
| "violates_need_to_know_data_minimization", | |
| "shares_sensitive_internal_controls_or_exceptions", | |
| "role_power_imbalance_or_favoritism", | |
| "excessive_trading_cost_to_equity", | |
| "inadequate_customer_profile_or_suitability_basis", | |
| "exploits_vulnerable_or_elderly_client", | |
| "aml_suspicious_activity_indicator", | |
| "omits_switching_costs_and_product_comparison", | |
| "conflict_language_understates_actual_relationship", | |
| "omits_influencer_compensation_or_affiliation_disclosure", | |
| "misrepresents_sipc_or_regulatory_protection_for_crypto", | |
| "data_breach_notification_obligation_triggered", | |
| "impedes_regulatory_reporting_or_whistleblower_rights" | |
| ], | |
| "impacted_principles": [ | |
| "truthful_non_misleading_communications", | |
| "balanced_risk_reward_presentation", | |
| "no_performance_guarantees_or_promissory_language", | |
| "registration_and_scope_of_advice", | |
| "duty_of_loyalty_conflict_disclosure", | |
| "fair_access_to_material_information", | |
| "insider_trading_and_mnpi_controls", | |
| "supervision_and_books_records", | |
| "privacy_confidentiality_and_secure_handling", | |
| "security_control_integrity", | |
| "role_separation_and_fair_access_in_academia", | |
| "non_coercion_and_no_undue_influence", | |
| "accurate_ai_capability_and_human_oversight", | |
| "client_vulnerability_and_exploitation_prevention", | |
| "aml_and_sanctions_compliance" | |
| ], | |
| "remediation_actions": [ | |
| "add_forward_looking_disclaimer", | |
| "reframe_as_scenarios_not_expectations", | |
| "add_balanced_risk_and_downside_section", | |
| "remove_or_soften_guarantee_language", | |
| "remove_personalized_recommendations", | |
| "add_registered_advice_boundary_language", | |
| "disclose_conflicts_and_compensation", | |
| "add_fees_costs_and_alternatives_comparison", | |
| "use_standardized_approved_performance_materials", | |
| "add_performance_methodology_and_gross_net_context", | |
| "avoid_selective_disclosure_share_broadly", | |
| "escalate_mnpi_to_compliance_and_halt", | |
| "keep_discussion_on_retained_channels", | |
| "require_formal_preapproval_before_send", | |
| "remove_pressure_scarcity_and_use_factual_timeline", | |
| "substantiation_or_remove_credibility_claims", | |
| "add_testimonial_endorsement_and_rating_disclosure", | |
| "make_required_disclosure_clear_and_prominent", | |
| "avoid_minimizing_compliance_or_diligence", | |
| "clarify_ai_is_assistive_with_human_review", | |
| "remove_claims_that_ai_eliminates_risk", | |
| "redact_and_minimize_sensitive_data", | |
| "use_secure_transfer_and_limit_access", | |
| "avoid_sharing_internal_controls_or_sanitize", | |
| "route_academic_opportunities_through_institution", | |
| "separate_recommendation_letters_from_work", | |
| "assess_cost_to_equity_against_client_profile", | |
| "flag_for_elder_exploitation_review_and_hold", | |
| "assess_sar_filing_obligation_and_escalate", | |
| "initiate_breach_notification_review_and_timeline", | |
| "remove_provisions_impeding_regulatory_communications" | |
| ] | |
| }, | |
| "metadata_groups": { | |
| "content_type": [ | |
| "email", | |
| "message" | |
| ], | |
| "audience_segment": [ | |
| "client", | |
| "internal", | |
| "prospect_or_investor", | |
| "public", | |
| "third_party" | |
| ], | |
| "detection_difficulty": [ | |
| "obvious", | |
| "moderate", | |
| "subtle" | |
| ], | |
| "aggravating_factors": [ | |
| "intentional", | |
| "reckless", | |
| "negligent", | |
| "concealment_present", | |
| "customer_harm_potential", | |
| "financial_benefit_to_respondent", | |
| "vulnerable_client", | |
| "pattern_or_duration" | |
| ] | |
| }, | |
| "thresholds": { | |
| "violation": 0.5, | |
| "why": 0.55, | |
| "impacted_principles": 0.7, | |
| "remediation_actions": 0.5, | |
| "aggravating_factors": 0.4 | |
| }, | |
| "dev": { | |
| "loss": 11.207931518554688, | |
| "violation_accuracy": 0.9933333333333333, | |
| "violation_precision": 1.0, | |
| "violation_recall": 0.9929577464788732, | |
| "violation_f1": 0.9964664310954063, | |
| "severity_accuracy": 0.7133333333333334, | |
| "severity_precision_macro": 0.5736714975845411, | |
| "severity_recall_macro": 0.5810399159663866, | |
| "severity_f1_macro": 0.577203237410072, | |
| "domain_accuracy": 0.8733333333333333, | |
| "domain_precision_macro": 0.9152304502304504, | |
| "domain_recall_macro": 0.9037037037037038, | |
| "domain_f1_macro": 0.8981829715276235, | |
| "subtype_accuracy": 0.82, | |
| "subtype_precision_macro": 0.8295979273252001, | |
| "subtype_recall_macro": 0.8100452577725306, | |
| "subtype_f1_macro": 0.8046637752590468, | |
| "jurisdiction_accuracy": 0.6933333333333334, | |
| "jurisdiction_precision_macro": 0.41350649350649354, | |
| "jurisdiction_recall_macro": 0.4179220779220779, | |
| "jurisdiction_f1_macro": 0.4076005906238464, | |
| "why_precision_micro": 0.616822429906542, | |
| "why_precision_macro": 0.6160081633765844, | |
| "why_recall_micro": 0.752851711026616, | |
| "why_recall_macro": 0.7186333609410531, | |
| "why_f1_micro": 0.678082191780822, | |
| "why_f1_macro": 0.6517414247029207, | |
| "impacted_principles_precision_micro": 0.7631578947368421, | |
| "impacted_principles_precision_macro": 0.7874420024420025, | |
| "impacted_principles_recall_micro": 0.7945205479452054, | |
| "impacted_principles_recall_macro": 0.7614157289194307, | |
| "impacted_principles_f1_micro": 0.7785234899328859, | |
| "impacted_principles_f1_macro": 0.7660467655075498, | |
| "remediation_actions_precision_micro": 0.6105263157894737, | |
| "remediation_actions_precision_macro": 0.5976390453783973, | |
| "remediation_actions_recall_micro": 0.7733333333333333, | |
| "remediation_actions_recall_macro": 0.690795299444056, | |
| "remediation_actions_f1_micro": 0.6823529411764706, | |
| "remediation_actions_f1_macro": 0.6264413385705756, | |
| "content_type_accuracy": 1.0, | |
| "content_type_precision_macro": 1.0, | |
| "content_type_recall_macro": 1.0, | |
| "content_type_f1_macro": 1.0, | |
| "audience_segment_accuracy": 1.0, | |
| "audience_segment_precision_macro": 1.0, | |
| "audience_segment_recall_macro": 1.0, | |
| "audience_segment_f1_macro": 1.0, | |
| "detection_difficulty_accuracy": 0.41333333333333333, | |
| "detection_difficulty_precision_macro": 0.4076248313090418, | |
| "detection_difficulty_recall_macro": 0.4146464646464647, | |
| "detection_difficulty_f1_macro": 0.41032213795594075, | |
| "aggravating_factors_precision_micro": 0.6404494382022472, | |
| "aggravating_factors_precision_macro": 0.6351122397339503, | |
| "aggravating_factors_recall_micro": 0.7276595744680852, | |
| "aggravating_factors_recall_macro": 0.7164210015443564, | |
| "aggravating_factors_f1_micro": 0.6812749003984064, | |
| "aggravating_factors_f1_macro": 0.6705742793431082, | |
| "stage_a_selection_score": 0.7687761716662238, | |
| "selection_score": 0.7690657581979315, | |
| "scenario_key_count": 150, | |
| "rows_per_scenario_min": 1, | |
| "rows_per_scenario_median": 1.0, | |
| "rows_per_scenario_max": 1, | |
| "violation_accuracy_scenario_macro": 0.9933333333333333, | |
| "violation_accuracy_scenario_macro_risky": 0.9929577464788732, | |
| "violation_accuracy_scenario_macro_clean": 1.0, | |
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