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---
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 |