wehub-resource-sync
18 lines
717 B
Python
18 lines
717 B
Python
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""" This example illustrates how to use the slim-sa-ner-tool, which is a 'combination' model that brings together
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both sentiment and named entity recognition in a single function-calling model. """
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from llmware.models import ModelCatalog
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model = ModelCatalog().load_model("slim-sa-ner-tool", sample=False, temperature=0.0)
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text = ("Tesla stock declined yesterday 8% in premarket trading after a poorly-received event in San Francisco "
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"yesterday, in which the company indicated a likely shortfall in revenue.")
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response = model.function_call(text, function="classify", params=["sentiment,person,organization,place"])
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print("response: ", response)
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ModelCatalog().tool_test_run("slim-sa-ner-tool")
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