NeuralChat-finetuned-for-fraud-detection

neural-chat-finetuned-bilic-v1

This model is a fine-tuned version of Intel/neural-chat-7b-v3-1 on our custom dataset.

Model description

This is a fine tuned version of the intel's Neuralchat model, specifically trained on a carefully curated dataset on fraud detection. We implemented a contextual based architecture to enable the model learn and be adept at understanding context within a conversation as opposed to the traditional rule based approach.

Intended uses & limitations

• detecting fraudulent conversations in real-time
• Giving a summary of conversations and suggestions
• Understanding with high accuracy the context in a conversation to make better predictions

Training

50,000 synthetically conversations

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• training_steps: 250
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.36.0.dev0
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0