car-review-absa

Model · Text classification

pyrrho-v2-nano-g1

Yan Fitzner

Pyrrho is a CPU-runnable co-processor for retrieval-augmented generation. It reads a question before retrieval to suggest what evidence to seek, then reads the question with retrieved passages to assess whether those passages support an answer. A surrounding RAG runtime decides whether to answer, retrieve again, or surface a conflict. The broader project is described in the The evidence verdict is SUFFICIENT, DISPUTED, or INSUFFICIENT. These are predictions about the supplied passages. The model does not retrieve sources, generate answers or citations, check external facts, or prove that a corpus has been searched completely. The two passes use the same encoder with different input…

A ~150M-parameter encoder that checks whether a RAG answer is supported by the source it was given. Given an answer and a source (and optionally the question), it returns grounded or hallucinated with P(grounded). It runs on CPU. - Every number below is traceable to a committed report in that repository. Hashes for every file are in the repository's MODELPROVENANCE.json. python -m eval.provenance --verify re-checks them. All measured on CPU against the pinned weights, using test sets rebuilt from pinned public datasets. F1 is for the hallucinated class, and the 95% CIs come from a 2,000-resample bootstrap. - 512 tokens reproduces the training run's published numbers (F1 0.6824, acc 0.7468…

Model · Text classification

Firebird-ModernBERT-512-RW

Noumenon, Inc.

Firebird-ModernBERT-512-RW is an experimental post-trained variant of It is a ~149M parameter ModernBERT binary classifier for distinguishing: - 0 — HUMAN The maximum sequence length is 512 tokens. This checkpoint was produced through reward-weighted classifier post-training. The original Firebird checkpoint was kept frozen as a reference model. Training examples were scored by the original classifier, difficult examples received larger loss weights, and the post-trained model was constrained against the frozen reference using a KL penalty. L = weightedcrossentropy + beta KL(reference || policy) Hard human examples received greater weighting than ordinary examples because one goal of the…

When a caller starts talking while a voice agent is still speaking, should the agent stop? This model makes that decision from text, in a few milliseconds. It reads one moment of a call: what the agent had already said, what it was about to say, and the caller's speech-recognised words. It then returns the probability that the agent should stop (INTERRUPT) or keep talking (CONTINUE). The CPU-friendly model: 38 ms per decision on 4 CPU threads. It matches the 400m on held-out business types and real calls, and gives up about 2 points under distribution shift. Have a GPU? bargein-classifier-ettin-400m is the flagship: more robust to distribution shift, at 8.5 ms on a GPU. Callers talk over…

Model · Text classification

tasksource-jev-nano-v0

Tasksource

High-Throughput Native Decision Model (<200M Parameters) Decoupled Multi-Vector Late Interaction for Fast, Calibrated System-One Decisions Tasksource-JEV-Nano is a compact (~149M parameter) decision model that picks the optimal action, category, or verdict from a candidate list using token-level multi-vector late interaction rather than conventional classification heads. Built on answerdotai/ModernBERT-base and lightonai/LateOn, it handles all three fundamental System-One decision types: 1. Choice: Selecting the best candidate among variable numbers of options ($K=2$ to $K=100+$). 2. NOUL: Yes/no uncertainty decisions. 3. Score: Quantitative ranking and graded scales. All evaluations below…

Model · Text classification

mailroom-modernbert-classifier

Lucius Morningstar

Hierarchical document classifier for the LLM-Mailroom intake pipeline: a fine-tuned ModernBERT-base encoder with a doctype head plus one subclass head per document class. It is the deterministic pre-check in the BERT-coupled intake overhaul (mailroom-issues #85). 8,192-token context, bf16. heads (contract, corporaterecord, correspondence, insuranceclaim, mergeragreement). MLP heads with dropout 0.1. by plurality vote over windows; subclass by plurality over windows whose doctype vote is the winning class. doctype (6): contract, mergeragreement, corporaterecord, correspondence, insuranceclaim, unknown (inference-only abstention — not a trained class) development, distributor, endorsement…