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2.0 KiB
Markdown
209 lines
2.0 KiB
Markdown
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# NER Extractor | [Start Chat](https://gptcall.net/chat.html?data=%7B%22contact%22%3A%7B%22id%22%3A%222tOUF5oANEQ_DWOXgCy_y%22%2C%22flow%22%3Atrue%7D%7D)
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This prompt is all about the extraction of the Named Entities from the specified content
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# Prompt
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```
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Extract the NER with the score in JSON format from the following content
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{{ content }}
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```
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## Welcome Message
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Here is the Named Entity Recognition (NER) entities and their scores in JSON format from the given content:
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```
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{
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"entities": [
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{
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"entity": "GPT models",
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"type": "ORG",
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"score": 0.89
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},
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{
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"entity": "DevOps",
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"type": "MISC",
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"score": 0.82
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},
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{
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"entity": "GPT-J",
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"type": "ORG",
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"score": 0.98
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},
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{
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"entity": "CPU",
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"type": "PRODUCT",
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"score": 0.89
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},
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{
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"entity": "GPU",
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"type": "PRODUCT",
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"score": 0.92
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},
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{
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"entity": "NVIDIA",
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"type": "ORG",
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"score": 0.95
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},
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{
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"entity": "RTX A6000",
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"type": "PRODUCT",
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"score": 0.97
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},
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{
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"entity": "A40",
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"type": "PRODUCT",
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"score": 0.94
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}
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]
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}
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```
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## Conversation
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**ASSISTANT**: Here is the Named Entity Recognition (NER) entities and their scores in JSON format from the given content:
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```
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{
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"entities": [
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{
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"entity": "GPT models",
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"type": "ORG",
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"score": 0.89
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},
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{
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"entity": "DevOps",
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"type": "MISC",
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"score": 0.82
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},
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{
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"entity": "GPT-J",
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"type": "ORG",
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"score": 0.98
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},
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{
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"entity": "CPU",
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"type": "PRODUCT",
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"score": 0.89
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},
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{
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"entity": "GPU",
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"type": "PRODUCT",
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"score": 0.92
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},
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{
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"entity": "NVIDIA",
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"type": "ORG",
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"score": 0.95
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},
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{
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"entity": "RTX A6000",
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"type": "PRODUCT",
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"score": 0.97
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},
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{
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"entity": "A40",
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"type": "PRODUCT",
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"score": 0.94
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}
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]
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}
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```
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