mDeBERTa-v3-base-mnli-xnli

Model · Zero-shot classification

mDeBERTa-v3-base-xnli-multilingual-nli-2mil7

Moritz Borrett-Laurer (formerly Laurer)

This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. The underlying mDeBERTa-v3-base model was pre-trained by Microsoft on the CC100 multilingual dataset with 100 languages. The model was then fine-tuned on the XNLI dataset and on the multilingual-NLI-26lang-2mil7 dataset. Both datasets contain more than 2.7 million hypothesis-premise pairs in 27 languages spoken by more than 4 billion people. As of December 2021, mDeBERTa-v3-base is the best performing multilingual base-sized transformer model introduced by Microsoft in this paper. This model was trained on the…

Model · Zero-shot classification

naluri-xlmr-base

Jericho Siahaya

Instinctive, multilingual decisions in one pass. Naluri (Indonesian for instinct; Neural Answering via Label Understanding and Rapid Inference) is a small System One decision model: about 280M parameters, built on XLM-RoBERTa base. You give it a text or JSON state and a typed question with its options written out. It returns calibrated probabilities over those options in a single forward pass, taking about 5 ms per question on a laptop GPU and about 50–60 ms on a CPU. It never generates text. It supports the same three question types as TypeSafe Jev and Laya: Code, training pipeline and full report: github.com/jerichosiahaya/naluri. - Option descriptions matter. Naluri reads them, so clear…

Model · Zero-shot classification

gliner-guard-omni

HiveTraceLab

One encoder model that replaces your entire guardrail stack: safety classification, PII detection, adversarial attack detection, intent and tone analysis — all in a single forward classification, NER and more · no LLM required Install dependencies Classify Harmful messages and Detect PII via single forward pass GLiNER Guard Omni fine-tunes fastino/gliner2-multi-v1 on our guardrail taxonomy while preserving its multilingual zero-shot generalization. You get GLiNER Guard's safety understanding on top of the base model's ability to handle labels and domains beyond the training set — so you can define custom policies with nothing but natural language descriptions. For specific usecases you can…

Classify text in 30+ languages into any labels you choose, with no training. Use it with the transformers zero-shot-classification pipeline, like facebook/bart-large-mnli, but multilingual, smaller, and with an 8k-token context window (fine-tuned at up to 1,024 tokens). non-commercial sets). See Training. Part of Horizon Labs' open models (collection). Labels: notentailment (0) and entailment (1). For each candidate label the model scores whether the text entails "This example is {label}." (or your hypothesistemplate). Any NLI-style use works too: pass text and textpair to a text-classification pipeline. Accuracy, single-label (multilabel=False: the label with the highest entailment score…

Model · Zero-shot classification

Vela-2.0-0.3B

vLLM Semantic Router

Open Foundation Routing Models Routing decisions. Safety checks. Precise text spans. Vela 2.0 brings routing questions and span-level decisions into one model interface. Supply your options, labels and rubrics at request time; ask about the request, its context and the answer together. The 0.3B is the family's compact multilingual encoder, with an 8,192-token input budget and Torch or ONNX inference on CPU or GPU. 1. Route with your own criteria. Choose a route, check a policy condition, assign a score or select multiple labels. 2. Locate the text behind a signal. Return spans of personal information and unsupported claims with character offsets and probabilities. 3. Ask across the whole…

Model · Zero-shot classification

siddhanto-bangla-english-banglish

Kazal Chandra Barman

Siddhanto (সিদ্ধান্ত, "decision") is a fine-tune of convaiinnovations/laya-multilingual (mmBERT-base, 322M params) for open-domain typed decisions over Bangla, English and Banglish (romanized Bangla / code-switched) conversational text. It is not a fixed-label classifier. It implements Laya's typed choice / noul / score interface: at request time you supply the question and its options (any schema, any domain), and the model returns a calibrated probability distribution over exactly those options — no retraining or head-swapping needed to add a new label set. Fine-tuned on ~268k typed-decision examples drawn from: - An internal 56-intent OTA (travel-agency) synthetic corpus…