Instructions to use mr-checker/SalesGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use mr-checker/SalesGen with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("mr-checker/SalesGen", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Sales Conversation Conversion Classifier
Overview
This model is designed to analyze a sales conversation between a customer and a sales representative and predict whether the conversation is associated with a potential conversion.The model is intended to serve as an initial classification layer for a broader lead-intelligence system. Its purpose is to help identify conversations that may require further attention from a sales team.
Intended Use
The model can be used for:
- Classifying sales conversations into conversion and non-conversion outcomes.
- Prioritizing conversations for further review.
- Supporting automated lead-identification and follow-up systems.
- Providing an initial prediction before structured customer information is extracted.
Model Variants
Two model versions were evaluated during development. The primary difference observed between the evaluated versions was the number of false-positive predictions:
- Model 1: 881 false positives
- Model 2: 854 false positives
The second evaluated model produced fewer false positives in the reported evaluation.
Evaluation
The models were evaluated using standard classification metrics and confusion-matrix results. The evaluation included:
- Precision
- Recall
- F1-score
- Accuracy
- ROC-AUC
- PR-AUC
- Confusion matrix
Classification Report
Image: Classification report of both the models are almost same (minute difference).
Limitations
The available evaluation is based on the selected sales-conversation dataset and its associated conversion outcome labels. The model's current prediction target represents conversion outcome and should not automatically be interpreted as a definitive business-level lead classification. Real-world performance may vary depending on:
- Industry
- Product or service
- Customer behaviour
- Conversation style
- Quality of transcripts
- Business-specific definitions of a qualified lead
Intended Role in the Larger System
The model is intended to act as a prediction component within a larger customer-intelligence system. Its output can be combined with structured information extraction and business rules to support sales operations and lead management.
Status
Development / Prototype The model is currently being evaluated as part of a hackathon prototype and may undergo further improvement with domain-specific and real-world conversation data.
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