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