---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-model-intent-classification
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-model-intent-classification

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.

## Model description

We have finetuned Base Bert model for text classification task. We used intent-detection dataset for traning our model. 

## Intended uses & limitations

More information needed

## How to use

Use below code to test the model

new_model = AutoModelForSequenceClassification.from_pretrained("ArunAIML/bert-model-intent-classification", 
                                                               num_labels=21,
                                                               id2label=id_to_label,
                                                               label2id=label_to_id)

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")



## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

### Label Maps used 

id_to_labels = 
{0: '100_NIGHT_TRIAL_OFFER', 1: 'ABOUT_SOF_MATTRESS', 2: 'CANCEL_ORDER', 3: 'CHECK_PINCODE', 4: 'COD', 5: 'COMPARISON', 6: 'DELAY_IN_DELIVERY', 7: 'DISTRIBUTORS', 8: 'EMI', 9: 'ERGO_FEATURES', 10: 'LEAD_GEN', 11: 'MATTRESS_COST', 12: 'OFFERS', 13: 'ORDER_STATUS', 14: 'ORTHO_FEATURES', 15: 'PILLOWS', 16: 'PRODUCT_VARIANTS', 17: 'RETURN_EXCHANGE', 18: 'SIZE_CUSTOMIZATION', 19: 'WARRANTY', 20: 'WHAT_SIZE_TO_ORDER'}
labels_to_id = 
{'100_NIGHT_TRIAL_OFFER': 0, 'ABOUT_SOF_MATTRESS': 1, 'CANCEL_ORDER': 2, 'CHECK_PINCODE': 3, 'COD': 4, 'COMPARISON': 5, 'DELAY_IN_DELIVERY': 6, 'DISTRIBUTORS': 7, 'EMI': 8, 'ERGO_FEATURES': 9, 'LEAD_GEN': 10, 'MATTRESS_COST': 11, 'OFFERS': 12, 'ORDER_STATUS': 13, 'ORTHO_FEATURES': 14, 'PILLOWS': 15, 'PRODUCT_VARIANTS': 16, 'RETURN_EXCHANGE': 17, 'SIZE_CUSTOMIZATION': 18, 'WARRANTY': 19, 'WHAT_SIZE_TO_ORDER': 20}

Please use above labels to reproduce results

### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1

### Results

The model was evaluated on a validation set. Below is the detailed classification report in a tabular format:

| Label                     | Precision | Recall | F1-Score | Support |
| :------------------------ | :-------- | :----- | :------- | :------ |
| `100_NIGHT_TRIAL_OFFER`   | 1.00      | 1.00   | 1.00     | 4       |
| `ABOUT_SOF_MATTRESS`      | 1.00      | 1.00   | 1.00     | 2       |
| `CANCEL_ORDER`            | 1.00      | 1.00   | 1.00     | 2       |
| `CHECK_PINCODE`           | 1.00      | 1.00   | 1.00     | 2       |
| `COD`                     | 1.00      | 1.00   | 1.00     | 2       |
| `COMPARISON`              | 0.33      | 0.50   | 0.40     | 2       |
| `DELAY_IN_DELIVERY`       | 1.00      | 1.00   | 1.00     | 2       |
| `DISTRIBUTORS`            | 1.00      | 1.00   | 1.00     | 7       |
| `EMI`                     | 0.89      | 1.00   | 0.94     | 8       |
| `ERGO_FEATURES`           | 1.00      | 1.00   | 1.00     | 2       |
| `LEAD_GEN`                | 1.00      | 1.00   | 1.00     | 4       |
| `MATTRESS_COST`           | 1.00      | 0.80   | 0.89     | 5       |
| `OFFERS`                  | 1.00      | 1.00   | 1.00     | 2       |
| `ORDER_STATUS`            | 1.00      | 0.75   | 0.86     | 4       |
| `ORTHO_FEATURES`          | 1.00      | 1.00   | 1.00     | 4       |
| `PILLOWS`                 | 1.00      | 1.00   | 1.00     | 2       |
| `PRODUCT_VARIANTS`        | 0.50      | 0.50   | 0.50     | 4       |
| `RETURN_EXCHANGE`         | 1.00      | 0.67   | 0.80     | 3       |
| `SIZE_CUSTOMIZATION`      | 0.50      | 0.50   | 0.50     | 2       |
| `WARRANTY`                | 0.67      | 1.00   | 0.80     | 2       |
| `WHAT_SIZE_TO_ORDER`      | 0.80      | 1.00   | 0.89     | 4       |
| **Accuracy** |           |        | **0.89** | **66** |
| **Macro Avg** | 0.90      | 0.89   | 0.89     | 66      |
| **Weighted Avg** | 0.91      | 0.89   | 0.90     | 66      |