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# 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
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "pipeline_tag": "text-classification", "model-index": [{"name": "roberta-finetuned-WebClassification", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-finetuned-WebClassification
===================================


This model is a fine-tuned version of xlm-roberta-base on the Web Classification Dataset.
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.


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



### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.1
* Datasets 1.18.4
* Tokenizers 0.11.6



### 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 ### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.1
* Datasets 1.18.4
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.1
* Datasets 1.18.4
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.1
* Datasets 1.18.4
* Tokenizers 0.11.6