| license: apache-2.0 | |
| base_model: Intel/neural-chat-7b-v3-1 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: neural-chat-finetuned-bilic-v1 | |
| results: [] | |
|  | |
| # neural-chat-finetuned-bilic-v1 | |
| This model is a fine-tuned version of [Intel/neural-chat-7b-v3-1](https://huggingface.co/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 | |