openjevx

Model · Text classification

laya-typed-decisions

Convai Innovations

Non-autoregressive System 1 decision model, fine-tuned on the typed-decisions workflows: agent-trace observability, customer service, invoice processing and security incidents. Part of the Laya family. 400 test cases, 2,000 decisions, measured on the official test split. +3.9 points over Jev's published 0.727, above the 0.735 teacher ceiling, with 2.4x better Brier and 1.6x better score MAE. Jev figures are third-party published, not measured here — there is no TypeSafe API access in this project, and sample sizes and prompts differ. Treat the comparison as indicative. Router will not select this checkpoint automatically unless you construct it with autotaskdetection=True — it is…

Model · Text classification

laya

Convai Innovations

Multilingual, non-autoregressive System 1 decision model. Give it a state (text, email, ticket, or JSON) and typed questions; it returns typed answers with mathematically calibrated probabilities in a single forward pass (~33 ms) across 100+ languages. Trained with reinforcement learning against strictly proper scoring rules (RLCD), so reporting honest probabilities is the only way to maximise reward. It never generates text, so there is nothing to parse and nothing to hallucinate. This repo holds all three checkpoints and is the hub for the family. The English checkpoint is at the repo root; the other two are bundled subfolders, and only the one you request is downloaded: pip install -U…

Model · Text classification

laya-kvp10k-noul

Sothiara Em

A fine-tuned Laya model (Convai Innovations, 421M, ModernBERT-large backbone) that answers one typed noul question: does a value correctly match its key label? (e.g. firstname = John → true, firstname = 1992 → false). Fine-tuned on the IBM KVP-10K dataset via the pre-parsed community mirror (OCR pre-extracted; no OCR step needed). This is the v2 (remediated) release: document-level 80/10/10 data partitioning, mixed-class frozen test set, calib-split temperature fitting, and a frozen category-stratified release gate. It supersedes the v1 release pre-remediation, historical artifact). Laya is a non-autoregressive decision model: you give it a state (text/dict) plus typed questions (choice…

Model · Text classification

afm-de

Ariacompute

Laya-layout ModernBERT + MASK option head, RLCD fine-tune from convaiinnovations/laya. Checkpoint files: model.safetensors, rlagentconfig.json, tokenizer/, encoder/. Load with AFM-D: python -m afmd.de.eval --checkpoint --device cuda. See AFM-D / product docs. License follows the base Laya / ModernBERT stack.

DEBATE-kor-large is a Korean-adapted Political DEBATE model for binary natural language inference (NLI) on political text. The model is initialized from mlburnham/PoliticalDEBATEDeBERTalargev1.1, the original DeBERTa-based Political DEBATE checkpoint, and subsequently fine-tuned on jongrock17/PolNLI-kor, a Korean translation and adaptation of PolNLI. Political DEBATE DeBERTa-large → PolNLI-kor → DEBATE-kor-large Unlike the PolNLI-kor-RoBERTa model family, which starts from Korean-pretrained KLUE-RoBERTa encoders, DEBATE-kor directly adapts the original Political DEBATE checkpoint to Korean political NLI. DEBATE-kor formulates NLI as a binary classification problem. notentailment combines…

This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 1000 - Transformers 5.12.1 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.22.2