| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: QueryCategorizer |
| results: [] |
| datasets: |
| - bitext/Bitext-customer-support-llm-chatbot-training-dataset |
| language: |
| - en |
| pipeline_tag: text-classification |
| --- |
| |
|
|
| # QueryCategorizer |
|
|
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the bitext/Bitext-customer-support-llm-chatbot-training-dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0038 |
| - Accuracy: 0.9996 |
| - Precision: 0.9996 |
| - Recall: 0.9996 |
| - F1: 0.9996 |
|
|
| ## Model description |
| A fine-tuned DistilBERT model that classifies customer support queries into one of 9 departments, |
| designed to auto-route incoming support tickets to the correct team. |
| ## Model Details |
|
|
| - **Base model:** distilbert-base-uncased |
| - **Task:** Multi-class text classification (9 classes) |
| - **Language:** English |
| - **Fine-tuned on:** bitext/Bitext-customer-support-llm-chatbot-training-dataset |
|
|
| The model predicts one of the following 9 departments: |
|
|
| ACCOUNT, CONTACT, DELIVERY, FEEDBACK, INVOICE, ORDER, PAYMENT, REFUND, SHIPPING |
|
|
|
|
| ## Intended uses & limitations |
|
|
| - Routing incoming customer support emails/messages to the appropriate department |
| - Prototyping multi-class text classification pipelines |
| - Educational demonstrations of fine-tuning transformer models for classification |
| - It is limited to just classify the message but not forward them to the appropriate department |
| - Out of 11 labels mentioned in dataset, we only took 9 and removed "Cancel" and "Subscription" because of limited no of data samples for that class. |
|
|
| ## Training and evaluation data |
|
|
| The bitext/Bitext-customer-support-llm-chatbot-training-dataset was used for training and evaluation of the model. |
| 80% was reserved for training, 10% for validation and 10% for testing. |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.0079 | 1.0 | 1125 | 0.0053 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | |
| | 0.0036 | 2.0 | 2250 | 0.0061 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | |
| | 0.0009 | 3.0 | 3375 | 0.0038 | 0.9996 | 0.9996 | 0.9996 | 0.9996 | |
| | 0.0005 | 4.0 | 4500 | 0.0038 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | |
| | 0.0001 | 5.0 | 5625 | 0.0029 | 0.9996 | 0.9996 | 0.9996 | 0.9996 | |
| | 0.0000 | 6.0 | 6750 | 0.0028 | 0.9996 | 0.9996 | 0.9996 | 0.9996 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 5.16.1 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.8.5 |
| - Tokenizers 0.23.1 |
