Action_Items Model README

Introduction

The Action_Items model is designed for classifying text into action items or non-action items using a fine-tuned version of distilbert. It is trained on a custom dataset and performs text classification tasks.

Architecture

The model utilizes DistilBERT, a smaller, faster, cheaper, and lighter version of BERT, optimized for text classification. It has been fine-tuned to specifically identify action items in text.

Training

The model was fine-tuned using a custom dataset labeled for action item classification. Key metrics for model evaluation include accuracy, precision, and recall, though specific validation and test metrics are not provided in the documentation.

Guide: Running Locally

To use the model locally, follow these steps:

1. Install the Transformers library if not already installed:

   pip install transformers
   

2. Load the model and run inference using the following code snippet:

   from transformers import pipeline
   
   summarizer = pipeline("text-classification", model="knkarthick/Action_Items")
   
   text = "Your input text here."
   result = summarizer(text)
   print(result)
   

3. Example Usage:

   • For classifying potential action items in a text about customer dependencies:

     text = '''
     Customer portion will have the dependency of, you know, fifty five probably has to be on XGEVA before we can start that track, but we can at least start the enablement track for sales and CSM who are as important as customers because they're the top of our funnel, especially sales.
     '''
     summarizer(text)
     

4. Consider utilizing cloud GPUs for faster processing, especially if dealing with large datasets or requiring real-time inference. Services like AWS, Google Cloud, or Azure provide such resources.

License

The Action_Items model is licensed under the Apache-2.0 License, allowing for wide usage and distribution.