Roberta-Webis-CPC Open Source Text Classification Model - Free Deployment for High-Accuracy Text Classification

Roberta Webis CPC

Developed by Amaan39

This model is a fine-tuned version of FacebookAI's roberta-base on the Webi-CPC-11 dataset, primarily used for text classification tasks, achieving an accuracy of 84.32% on the evaluation set.

Text Classification

Transformers

Open Source License:MIT #RoBERTa fine-tuning #Text classification #High accuracy

Downloads 47

Release Time : 3/16/2025

Model Overview

A fine-tuned model based on the RoBERTa architecture, suitable for text classification tasks and performs exceptionally well on specific domain datasets.

Model Features

High accuracy

Achieved 84.32% accuracy on the Webi-CPC-11 evaluation set

Based on RoBERTa architecture

Utilizes the optimized RoBERTa-base architecture with strong text comprehension capabilities

Domain adaptation

Fine-tuned on specific domain datasets, suitable for professional text classification tasks

Model Capabilities

Text classification

Natural language understanding

Domain-specific text analysis

Use Cases

Content classification

News classification

Classify news articles into predefined categories

Achieved 84.32% accuracy on the test set

Document categorization

Automatically classify and organize professional documents

🚀 Transformers Model

This project is a fine - tuned model based on the transformers library. It uses the roberta - base model from FacebookAI and is trained on the Webi - CPC - 11 dataset, achieving good accuracy on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of [FacebookAI/roberta - base](https://huggingface.co/FacebookAI/roberta - base) on an Webi - CPC - 11 dataset. It achieves the following results on the evaluation set:

Loss: 1.5703
Accuracy: 0.8432

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e - 05
train_batch_size: 32
eval_batch_size: 32
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
lr_scheduler_warmup_steps: 500
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	197	0.4964	0.6875
No log	2.0	394	0.4895	0.8326
0.4856	3.0	591	0.3711	0.8422
0.4856	4.0	788	0.3289	0.8464
0.4856	5.0	985	0.4112	0.8559
0.2928	6.0	1182	0.5872	0.8453
0.2928	7.0	1379	0.5353	0.8284
0.1493	8.0	1576	0.6069	0.8273
0.1493	9.0	1773	0.9225	0.8464
0.1493	10.0	1970	1.3133	0.8422
0.0641	11.0	2167	1.2524	0.8369
0.0641	12.0	2364	1.1893	0.8347
0.0394	13.0	2561	1.3631	0.8358
0.0394	14.0	2758	1.1922	0.8273
0.0394	15.0	2955	1.2648	0.8316
0.0205	16.0	3152	1.0889	0.8422
0.0205	17.0	3349	1.2235	0.8422
0.0094	18.0	3546	1.4707	0.8358
0.0094	19.0	3743	1.3305	0.8475
0.0094	20.0	3940	1.4021	0.8263
0.0151	21.0	4137	1.2689	0.8358
0.0151	22.0	4334	1.4997	0.8273
0.0061	23.0	4531	1.4872	0.8358
0.0061	24.0	4728	1.5773	0.8347
0.0061	25.0	4925	1.6127	0.8358
0.0037	26.0	5122	1.5534	0.8326
0.0037	27.0	5319	1.5532	0.8453
0.0036	28.0	5516	1.4986	0.8432
0.0036	29.0	5713	1.5698	0.8422
0.0036	30.0	5910	1.5703	0.8432

Framework versions

Transformers 4.48.3
Pytorch 2.6.0+cu124
Tokenizers 0.21.0

📄 License

This project is licensed under the MIT license.

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