| | --- |
| | 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 |
| | |
| | |
