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  slim-sentiment has been fine-tuned for **named entity extraction** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
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-     `{"people": [" ... "], "organization":[" ... "], "location": [" ... "]}`
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  SLIM models are designed to provide a flexible natural language generative model that can be used as part of a multi-step, multi-model LLM-based automation workflow.
 
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  slim-sentiment has been fine-tuned for **named entity extraction** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
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+     `{"people": ["..."], "organization":["..."], "location": ["..."]}`
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  SLIM models are designed to provide a flexible natural language generative model that can be used as part of a multi-step, multi-model LLM-based automation workflow.