| --- |
| datasets: |
| - financial_phrasebank |
| - clinc_oos |
| - hate_speech_offensive |
| tags: |
| - finance |
| language: |
| - en |
| --- |
| # 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](https://arxiv.org/abs/1810.04805) and first released in |
| [this repository](https://github.com/google-research/bert). |
| ## 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 |
| |
| |
