| --- |
| 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 | |
