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
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