Action_Items Open-source Text Classification Model - Free Deployment for Accurate Identification of Text Action Items

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Action Items

Developed by knkarthick

This is a text classification model fine-tuned on DistilBERT for identifying action items in text.

Text Classification

Transformers

EnglishOpen Source License:Apache-2.0 #Action Item Recognition #Meeting Minutes Analysis #Business Text Classification

Downloads 610

Release Time : 10/20/2022

Model Overview

The model is fine-tuned on DistilBERT to classify text as either action items (statements requiring action) or non-action items.

Model Features

Efficient and Lightweight

Based on the DistilBERT architecture, it maintains high performance while reducing model size and computational resource requirements.

Action Item Recognition

Specifically optimized for identifying statements in text that require action.

Chinese Language Support

Optimized and trained for Chinese text.

Model Capabilities

Text Classification

Action Item Recognition

Chinese Text Processing

Use Cases

Project Management

Meeting Minutes Analysis

Automatically identifies tasks and action items that need follow-up in meeting records.

Improves meeting efficiency and ensures important tasks are not overlooked.

Workflow Optimization

Extracts specific actions to be executed from work communications.

Helps teams better understand their responsibilities and tasks.

Customer Service

Customer Feedback Processing

Identifies issues in customer feedback that require immediate response or resolution.

Improves customer satisfaction and speeds up issue resolution.

🚀 Action Item Classification Model

This model is fine - tuned from 'distilbert' using a custom dataset for action item classification, helping to distinguish whether a text is an action item or not.

🚀 Quick Start

The model is obtained by fine - tuning 'distilbert' with a custom dataset!

LABEL_0 - Not an Action Item
LABEL_1 - Action Item

✨ Features

Tags: distilbert, seq2seq, text - classification
License: apache - 2.0
Datasets: Custom
Metrics: Accuracy, Precision, Recall

📦 Model Information

Property	Details
Model Type	Action_Items
Training Data	Custom

📚 Model Results

Task	Dataset	Metrics
Action Item Classification	Custom	Validation Accuracy
Action Item Classification	Custom	Validation Precision
Action Item Classification	Custom	Validation Recall
Action Item Classification	Custom	Test Accuracy
Action Item Classification	Custom	Test Precision
Action Item Classification	Custom	Test Recall

💻 Usage Examples

Basic Usage

The following code examples demonstrate how to use the model for text classification:

# Example 1
from transformers import pipeline
summarizer = pipeline("text-classification", model="knkarthick/Action_Items")
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)

# Example 2
from transformers import pipeline
summarizer = pipeline("text-classification", model="knkarthick/Action_Items")
text = '''
India, officially the Republic of India, is a country in South Asia.
'''
summarizer(text)

# Example 3
from transformers import pipeline
summarizer = pipeline("text-classification", model="knkarthick/Action_Items")
text = '''
We have been running the business successfully for over a decade now.
'''
summarizer(text)

📄 License

This model is licensed under the apache - 2.0 license.

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