NuMind 🔥 的結構化資訊提取模型

NuExtract 是 phi-3-mini 的一個版本，針對資訊提取進行了微調，使用高品質的私有合成數據集。要使用此模型，請提供輸入文本（少於 2000 個 tokens）和一個 JSON 模板，描述您需要提取的資訊。

注意：此模型純粹為提取式模型，因此模型輸出的所有文本都與原始文本中的內容相同。您也可以提供輸出格式範例，以幫助模型更精確地理解您的任務。

使用方式

提示詞格式

當使用特定的提示詞格式來提取文本時，此模型效果最佳

### 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/

參考資料

Hugging Face