Mistral workflow with text models #1794
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@BP-Ent please review this guide. |
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@BP-Ent Can you please quickly review this guide? Thanks. |
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- Introduction to Mistral model
- Mistral Implementation in
arcgis.learn - Install the model backbone
- Mistral with
TextClassifiermodel - Import the
TextClassifierclass fromarcgis.learn.textmodule - Initialize the
TextClassifiermodel with databunch - Initialize the
TextClassifiermodel without databunch - Classify text using the mistral model
- Load the model
- Save the model
- Mistral with the
EntityRecognizermodel - Import the
EntityRecognizerclass from thearcgis.learn.textmodule - Initialize the
EntityRecognizermodel with databunch - Initialize the
EntityRecognizermodel without databunch - Extract entities using the mistral model
- Load the model
- Save the model
- Conclusion
- References
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Mistral 7B is a decoder-based language model trained using almost 7 billion parameters designed to deliver both efficiency and high performance for real-world applications.
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Below are the parameters to be passed into EntityRecognizer :
backbone: To use mistral as the model backbone, use backbone="mistral".
examples: User defined examples for the mistral model, in python list format:
prompt: Text string describing the task and its guardrails. This is an optional parameter.
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To extract named entities using the mistral model, use the extract_entities method from the EntityRecognizer class. The input to the method will be a text string or a list of text strings.
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To load a saved mistral model, use the from_model method from the EntityRecognizer class.
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In this guide we demonstrated the steps to initialize and perform inference using the Mistral LLM as a backbone with the TextClassifier and EntityRecognizer models in arcgis.learn.
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@BP-Ent I have made the suggested changes, thanks. |
Guide for Mistral workflow with text models