BKnock-RoBERTa-v4

Model Card: BKnock-RoBERTa-v4

Model Details

• Model name: BKnock-RoBERTa-v4
• Base model: roberta-base
• Architecture: shared encoder with multitask classification heads
• Version: 4.0.0 (stable/frozen release)
• Version metadata: version_metadata.json

Intended Use

This model is intended for:

• research on text information dynamics
• structured text signal extraction for downstream statistical models
• temporal analysis of risk/sentiment/behavior/adaptation states
• exploratory alignment studies with subsequent temporal windows

Out-of-Scope Use

• operational decision automation
• use as prescriptive advice

Literature-Aligned Scope

The model scope follows empirical literature on efficiency and sentiment effects, which is mixed and time-varying rather than universally predictive. This model should therefore be used for behavioral representation and hypothesis generation, not for deterministic forecasting claims.

Tasks and Labels

• risk_level: low, medium, high, critical
• sentiment: negative, neutral, positive
• market_behavior: accumulation, distribution, panic_selling, euphoric_buying, uncertainty, regulatory_pressure
• adaptation_signal: stable, reactive, adaptive, unstable

Dataset Assumptions

Expected schema:

• textual content with UTC timestamp and asset symbol
• source categorization (social, news, blog, regulatory, developer)
• supervised labels for all four tasks

Data ingestion assumes strict schema and temporal integrity.

Temporal Leakage Risks

• random splitting is disallowed
• label distribution and language use can drift over time
• time-slice performance should be monitored before any downstream use

Limitations and Bias

• annotation subjectivity can bias class boundaries
• source imbalance can overrepresent high-volume channels
• crisis periods can alter language patterns and calibration
• model confidence is not equivalent to causal certainty
• documented dependencies can decay across assets and time regimes

Evaluation Reporting

The evaluation pipeline reports:

• per-task accuracy and macro-F1
• confusion matrices
• calibration curves and expected calibration error
• time-slice metrics
• drift diagnostics

Citation

@misc{bknock_roberta_v4_2026,
  title={BKnock-RoBERTa-v4: Multitask NLP for Behavioral Signal Extraction},
  author={BKnock Team},
  year={2026},
  note={Version 4.0.0}
}