BERT Base Intent model
This is a fine tuned model based on Bert-Base-Uncased model. This model is used to classify intent into 3 categories- Fintech, Out of Scope and Abusive. The base line model is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-5
- num_epochs: 3
- weight_decay:0.01
Training results
| Training Loss | Epoch | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|
| 0.114200 | 1.0 | 0.034498 | 0.991351 | 0.991346 |
| 0.024100 | 2.0 | 0.037945 | 0.992349 | 0.992355 |
| 0.009800 | 3.0 | 0.034846 | 0.993347 | 0.993345 |
Model Description
The finetuned Hugging Face model is a variant of the BERT-base-uncased architecture, trained for intent classification with three labels: fintech, abusive, and out of scope. The model has undergone a fine-tuning process, where it has been trained on a large corpus of annotated data using a supervised learning approach. The objective of the model is to classify incoming text data into one of the three predefined classes based on the underlying intent of the text.
The performance of the model was evaluated and it achieved high accuracy and F1 scores for all three classes. The model's high accuracy and robustness make it suitable for use in real-world applications, such as chatbots, customer service automation, and social media monitoring. Overall, the finetuned Hugging Face model provides an effective and reliable solution for intent classification with three labels: fintech, abusive, and out of scope.
- Developed by: Jeswin MS, Venkatesh R, Kushal S Ballari
- Model type: Intent Classification
- Language(s) (NLP): English
- Finetuned from model: Bert-base-uncased
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