| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: roberta-finetuned-WebClassification |
| results: [] |
| pipeline_tag: text-classification |
| --- |
| |
| <!-- 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. --> |
|
|
| # roberta-finetuned-WebClassification |
|
|
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Web Classification Dataset](https://www.kaggle.com/datasets/hetulmehta/website-classification). |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3473 |
| - Accuracy: 0.9504 |
| - F1: 0.9504 |
| - Precision: 0.9504 |
| - Recall: 0.9504 |
|
|
| ## Model description |
|
|
| The model classifies websites into the following categories: |
| - "0": "Adult", |
| - "1": "Business/Corporate", |
| - "2": "Computers and Technology", |
| - "3": "E-Commerce", |
| - "4": "Education", |
| - "5": "Food", |
| - "6": "Forums", |
| - "7": "Games", |
| - "8": "Health and Fitness", |
| - "9": "Law and Government", |
| - "10": "News", |
| - "11": "Photography", |
| - "12": "Social Networking and Messaging", |
| - "13": "Sports", |
| - "14": "Streaming Services", |
| - "15": "Travel" |
|
|
| ## Intended uses & limitations |
|
|
| Web classification in English (for now). |
|
|
| ## Training and evaluation data |
|
|
| Trained and tested on a 80/20 split of the [Web Classification Dataset](https://www.kaggle.com/datasets/hetulmehta/website-classification). |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | No log | 1.0 | 141 | 0.9315 | 0.8617 | 0.8617 | 0.8617 | 0.8617 | |
| | No log | 2.0 | 282 | 0.4956 | 0.9007 | 0.9007 | 0.9007 | 0.9007 | |
| | No log | 3.0 | 423 | 0.4142 | 0.9184 | 0.9184 | 0.9184 | 0.9184 | |
| | 0.9036 | 4.0 | 564 | 0.3998 | 0.9255 | 0.9255 | 0.9255 | 0.9255 | |
| | 0.9036 | 5.0 | 705 | 0.3235 | 0.9397 | 0.9397 | 0.9397 | 0.9397 | |
| | 0.9036 | 6.0 | 846 | 0.3631 | 0.9397 | 0.9397 | 0.9397 | 0.9397 | |
| | 0.9036 | 7.0 | 987 | 0.3705 | 0.9362 | 0.9362 | 0.9362 | 0.9362 | |
| | 0.0898 | 8.0 | 1128 | 0.3469 | 0.9468 | 0.9468 | 0.9468 | 0.9468 | |
| | 0.0898 | 9.0 | 1269 | 0.3657 | 0.9326 | 0.9326 | 0.9326 | 0.9326 | |
| | 0.0898 | 10.0 | 1410 | 0.3473 | 0.9504 | 0.9504 | 0.9504 | 0.9504 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.16.2 |
| - Pytorch 1.9.1 |
| - Datasets 1.18.4 |
| - Tokenizers 0.11.6 |
