Based on LFM2-1.2B, LFM2-1.2B-Extract is designed to extract important information from a wide variety of unstructured documents (such as articles, transcripts, or reports) into structured outputs like JSON, XML, or YAML. — Extracting invoice details from emails into structured JSON. — Converting regulatory filings into XML for compliance systems. — Transforming customer support tickets into YAML for analytics pipelines. — Populating knowledge graphs with entities and attributes from unstructured reports. You can find more information about other task-specific models in this blog post. Generation parameters: We strongly recommend using greedy decoding with a temperature=0. System prompt: If no system prompt is provided, the model will default to JSON outputs. We recommend providing a system prompt with a specific format (JSON, XML, or YAML) and a given schema to improve accuracy (see the following example). Supported languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish. Chat template: LFM2 uses a ChatML-like chat template as follows: You can automatically apply it using the dedicated .applychattemplate() function from Hugging Face…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: onnx-community
Теги: transformers.js, onnx, lfm2, liquid, edge, conversational, en, ar
Лайков: 4 | Загрузок: 21
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
