title: Ottema
emoji: 🏢
colorFrom: blue
colorTo: pink
sdk: static
pinned: true
license: apache-2.0
Ottema
Open, specialized AI models for Brazilian Portuguese and reliable AI systems.
We build open-source models that solve concrete production problems in Brazilian Portuguese. Our work focuses on open-vocabulary information extraction, structured-output recovery for agents, speech recognition, and operational AI for real-world workflows.
Based in Brazil. Open research, reproducible benchmarks, production-oriented models.
Brazilian Portuguese Extraction
Open-vocabulary NER and evidence extraction for real PT-BR text — including noisy operational domains where standard models fail.
| Model | What it's for | Result |
|---|---|---|
ottema/gliner2-ptbr-harem (v0.12b) |
NER on journalistic/formal PT-BR. Best entity F1 among compared models on HAREM. | entity F1 = 0.4749 (macro) / 0.4501 (micro), 4x faster than BERT-CRF |
ottema/gliner2-ptbr (v0.4) |
Generalist NER for informal PT-BR (chat, atendimento, suporte). | entity F1 = 0.9976 on synthetic benchmark |
ottema/gliner2-ptbr-ontoevidence (v0.18) |
Ontology-guided evidence extraction with hard-negative rejection. First model to break the GLiNER2 "yes-man" failure mode. | F1 = 0.32 on OE test, avg 4.4 pred/text |
ottema/gliner2-ptbr-ontoevidence-data |
2268 samples, 3 splits, multi-label spans + hard negatives. | Apache-2.0 |
💡 Also published as
ottema/gliner2-ptbr-v23(same weights, 90+ downloads).
Browse the full Ottema Open Models collection: huggingface.co/collections/ottema/ottema/ottema-open-models-6a3600e6cc0bdc9c01dd68c8
Reliable Agents
Small specialized models that recover structured output when the LLM fails — JSON, tool calls, schema-constrained generation.
| Model | What it does |
|---|---|
ottema/structfix-codet5p-220m |
Repairs broken JSON/tool-call output from upstream LLMs against a target schema. 220M params, fast, deterministic. |
ottema/structfix-bench |
Benchmark: 250k examples of schema-guided generation with controlled noise and constraint coverage. |
ottema/constraint-dsl |
Compact DSL for declaring typed constraints over JSON outputs. |
Speech & Edge AI
Lightweight ASR and small models for resource-constrained deployment.
| Model | What it's for |
|---|---|
ottema/stt_pt_quartznet15x5_ctc_small |
Research baseline / lightweight CPU ASR reference for PT-BR. See Nemotron full-stack for current SOTA. |
How we work
- Open research, reproducible benchmarks. Every model ships with training data, evaluation scripts, and ablations documented.
- Production-oriented. We optimize for the metric that matters in deployment: latency, label F1 on the user's actual distribution, hard-negative robustness.
- Honest about failure modes. We publish what didn't work (see the "yes-man problem" with OntoEvidence).
- No private data. All training data is either synthetic, openly licensed, or used for training only (not redistributed).
Try it
ottema/gliner2-ptbr-demo— interactive Gradio demo with model selection, label presets, and 7 example sentences spanning journalistic, informal, and operational Portuguese.ottema/structfix-demo— repair broken JSON / tool-call output against typed schemas. 5 schema presets, 10 broken-output presets, 13 paired examples.
Credits
Our models build on:
- GLiNER / GLiNER2 (Urchade Zaratiana et al.) — open-vocabulary NER architecture
- fastino/gliner2-multi-v1 (Fastino) — multilingual GLiNER2 base
- microsoft/mdeberta-v3-base — multilingual encoder
- CodeT5+ (Salesforce) — seq2seq for structured output repair
- Linguateca HAREM, lfcc, arubenruben — Portuguese NER datasets (training only)
License
All models and datasets are released under Apache-2.0 unless otherwise noted.
Contact
Organization: huggingface.co/ottema