Phi 3 Mini 4k Instruct Graph

🚀 Phi-3-mini-4k-instruct-graph模型卡片

本模型是微軟Phi-3-mini-4k-instruct的微調版本，專門用於從通用文本數據中進行實體關係提取。它旨在在生成實體關係圖方面達到與GPT - 4相當的質量和準確性，同時在大規模處理時提高效率。

🚀 快速開始

以下代碼片段展示瞭如何在GPU上快速運行該模型：

import torch 
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline 

torch.random.manual_seed(0) 
model = AutoModelForCausalLM.from_pretrained( 
    "EmergentMethods/Phi-3-mini-4k-instruct-graph",  
    device_map="cuda",  
    torch_dtype="auto",  
    trust_remote_code=True,  
) 

tokenizer = AutoTokenizer.from_pretrained("EmergentMethods/Phi-3-mini-4k-instruct-graph") 

messages = [ 
    {"role": "system", "content": """
A chat between a curious user and an artificial intelligence Assistant. The Assistant is an expert at identifying entities and relationships in text. The Assistant responds in JSON output only.

The User provides text in the format:

-------Text begin-------
<User provided text>
-------Text end-------

The Assistant follows the following steps before replying to the User:

1. **identify the most important entities** The Assistant identifies the most important entities in the text. These entities are listed in the JSON output under the key "nodes", they follow the structure of a list of dictionaries where each dict is:

"nodes":[{"id": <entity N>, "type": <type>, "detailed_type": <detailed type>}, ...]

where "type": <type> is a broad categorization of the entity. "detailed type": <detailed_type>  is a very descriptive categorization of the entity.

2. **determine relationships** The Assistant uses the text between -------Text begin------- and -------Text end------- to determine the relationships between the entities identified in the "nodes" list defined above. These relationships are called "edges" and they follow the structure of:

"edges":[{"from": <entity 1>, "to": <entity 2>, "label": <relationship>}, ...]

The <entity N> must correspond to the "id" of an entity in the "nodes" list.

The Assistant never repeats the same node twice. The Assistant never repeats the same edge twice.
The Assistant responds to the User in JSON only, according to the following JSON schema:

{"type":"object","properties":{"nodes":{"type":"array","items":{"type":"object","properties":{"id":{"type":"string"},"type":{"type":"string"},"detailed_type":{"type":"string"}},"required":["id","type","detailed_type"],"additionalProperties":false}},"edges":{"type":"array","items":{"type":"object","properties":{"from":{"type":"string"},"to":{"type":"string"},"label":{"type":"string"}},"required":["from","to","label"],"additionalProperties":false}}},"required":["nodes","edges"],"additionalProperties":false}
     """}, 
    {"role": "user", "content": """
-------Text begin-------
OpenAI is an American artificial intelligence (AI) research organization founded in December 2015 and headquartered in San Francisco, California. Its mission is to develop "safe and beneficial" artificial general intelligence, which it defines as "highly autonomous systems that outperform humans at most economically valuable work".[4] As a leading organization in the ongoing AI boom,[5] OpenAI is known for the GPT family of large language models, the DALL-E series of text-to-image models, and a text-to-video model named Sora.[6][7] Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI.
-------Text end-------
"""}
] 

pipe = pipeline( 
    "text-generation", 
    model=model, 
    tokenizer=tokenizer, 
) 

generation_args = { 
    "max_new_tokens": 500, 
    "return_full_text": False, 
    "temperature": 0.0, 
    "do_sample": False, 
} 

output = pipe(messages, **generation_args) 
print(output[0]['generated_text'])

# Output:

# {
#     "nodes": [
#         {
#             "id": "OpenAI",
#             "type": "organization",
#             "detailed_type": "ai research organization"
#         },
#         {
#             "id": "GPT family",
#             "type": "technology",
#             "detailed_type": "large language models"
#         },
#         {
#             "id": "DALL-E series",
#             "type": "technology",
#             "detailed_type": "text-to-image models"
#         },
#         {
#             "id": "Sora",
#             "type": "technology",
#             "detailed_type": "text-to-video model"
#         },
#         {
#             "id": "ChatGPT",
#             "type": "technology",
#             "detailed_type": "generative ai"
#         },
#         {
#             "id": "San Francisco",
#             "type": "location",
#             "detailed_type": "city"
#         },
#         {
#             "id": "California",
#             "type": "location",
#             "detailed_type": "state"
#         },
#         {
#             "id": "December 2015",
#             "type": "date",
#             "detailed_type": "foundation date"
#         },
#         {
#             "id": "November 2022",
#             "type": "date",
#             "detailed_type": "release date"
#         }
#     ],
#     "edges": [
#         {
#             "from": "OpenAI",
#             "to": "San Francisco",
#             "label": "headquartered in"
#         },
#         {
#             "from": "San Francisco",
#             "to": "California",
#             "label": "located in"
#         },
#         {
#             "from": "OpenAI",
#             "to": "December 2015",
#             "label": "founded in"
#         },
#         {
#             "from": "OpenAI",
#             "to": "GPT family",
#             "label": "developed"
#         },
#         {
#             "from": "OpenAI",
#             "to": "DALL-E series",
#             "label": "developed"
#         },
#         {
#             "from": "OpenAI",
#             "to": "Sora",
#             "label": "developed"
#         },
#         {
#             "from": "OpenAI",
#             "to": "ChatGPT",
#             "label": "released"
#         },
#         {
#             "from": "ChatGPT",
#             "to": "November 2022",
#             "label": "released in"
#         }
#     ]
# }

✨ 主要特性

• 本模型專為從通用文本數據中生成表示實體關係的結構化JSON數據而設計。
• 可用於增強各種文本數據庫中的信息檢索。
• 有助於探索不同類型文檔中的時間關係和演變敘事。
• 可用於對各種文本來源進行趨勢分析的高級預測建模。
• 特別適用於需要高吞吐量處理大量文本數據的應用，如內容聚合平臺、研究數據庫和綜合文本分析系統。

📚 詳細文檔

模型詳情

• 開發者：Emergent Methods
• 資助方：Emergent Methods
• 共享方：Emergent Methods
• 模型類型：microsoft/phi - 3 - mini - 4k - instruct（微調版）
• 語言：英語
• 許可證：知識共享署名-非商業性使用-相同方式共享4.0許可協議
• 微調基礎模型：[microsoft/phi - 3 - mini - 4k - instruct](https://huggingface.co/microsoft/phi - 3 - mini - 4k - instruct)

更多信息，請查看我們的博客文章： 📰 博客

用途

本模型旨在生成表示通用文本數據中實體關係的結構化JSON數據，可用於以下方面：

• 增強各種文本數據庫中的信息檢索。
• 探索不同類型文檔中的時間關係和演變敘事。
• 對各種文本來源進行趨勢分析的高級預測建模。

該模型特別適用於需要高吞吐量處理大量文本數據的應用，如內容聚合平臺、研究數據庫和綜合文本分析系統。

偏差、風險和侷限性

儘管數據集的目標是減少偏差並提高多樣性，但它仍然偏向於西方語言和國家。這一侷限性源於Llama2在翻譯和摘要生成方面的能力。此外，由於使用Llama2對開放網絡文章進行摘要，Llama2訓練數據中的任何偏差也會存在於該數據集中。同樣，微軟Phi - 3中存在的任何偏差也會出現在當前數據集中。

訓練詳情

• 訓練數據：來自AskNews的7000多篇故事和更新，經過精心策劃以避免主題重疊。
• 訓練過程：使用Transformers庫、SFTTrainer、PEFT和QLoRA進行微調。

評估結果

與GPT - 4o（基準真相）、Claude Sonnet 3.5和基礎Phi - 3模型相比：

環境影響

• 硬件類型：1x A100 SXM
• 使用時長：3小時
• 碳排放：0.44千克（根據機器學習影響計算器）

倫理考量

用戶應注意，本模型專為從通用文本數據中進行實體關係提取而設計，未經進一步微調可能不適用於其他領域。在將模型輸出用於可能影響決策或公共信息傳播的應用時，應進行審查和驗證。

📄 許可證

本模型使用的許可證為知識共享署名-非商業性使用-相同方式共享4.0許可協議（Creative Commons Attribution Non Commercial Share Alike 4.0）。