NuMind 结构化提取模型 🔥

NuExtract 是 phi-3-mini 的一个版本，在一个私有的高质量合成数据集上针对信息提取进行了微调。要使用该模型，请提供输入文本（少于 2000 个标记）和描述您需要提取的信息的 JSON 模板。

注意：此模型是纯粹的提取式模型，因此模型输出的所有文本都按原样存在于原始文本中。您还可以提供输出格式的示例，以帮助模型更准确地理解您的任务。

用法

提示格式

此模型在使用特定提示格式提取文本时效果最佳

### Template:
{
    "Model": {
        "Name": "",
        "Number of parameters": "",
    },
    "Usage": {
        "Use case": [],
        "Licence": ""
    }
}
### Example:
{
    "Model": {
        "Name": "Llama3",
        "Number of parameters": "8 billion",
    },
    "Usage": {
        "Use case":[
			"chat",
			"code completion"
		],
        "Licence": "Meta Llama3"
    }
}
### Text:
We introduce Mistral 7B, a 7–billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms the best open 13B model (Llama 2) across all evaluated benchmarks, and the best released 34B model (Llama 1) in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and automated benchmarks. Our models are released under the Apache 2.0 license. 

Code: https://github.com/mistralai/mistral-src 
Webpage: https://mistral.ai/news/announcing-mistral-7b/

参考

Hugging Face