Update README.md

README.md

@@ -11,92 +11,98 @@ base_model: FacebookAI/roberta-base
 pipeline_tag: text-classification
 ---
 
-# WebSector-Flexible Model Card
+# WebSector-Flexible
 
-## Model Overview
+## Model description
 
-The WebSector-Flexible model is a transformer-based multi-sector website classification model, fine-tuned using the RoBERTa architecture with LoRA and trained on the WebSector dataset, consisting of 109,476 websites in the training set. The flexible mode is designed to maximize recall by identifying both primary and secondary sectors of websites, making it suitable for applications that require broad coverage across multiple sectors. This mode is ideal for exploratory tasks or when it's critical to capture all possible sector associations.
+The **WebSector-Flexible** model is a RoBERTa-based transformer designed for high-recall website classification into one of ten broad sectors. It is part of the WebSector framework, which introduces a Single Positive Label (SPL) paradigm for multi-label classification using only the primary sector of websites. The flexible mode of this model focuses on maximizing recall by identifying both primary and secondary sectors, making it ideal for exploratory tasks or when it's critical to capture all possible sector associations.
 
-## Model Details
+## Intended uses & limitations
 
-- **Model type**: Transformer-based (RoBERTa + LoRA)
-- **Training dataset**: WebSector Corpus (Training set: 109,476 websites)
-- **Prediction modes**: Flexible mode
-- **Task**: Multi-sector website classification
-- **Architecture**: RoBERTa transformer fine-tuned with LexRank summarization for handling lengthy content
-- **Special Technique**: Single Positive Label (SPL) paradigm for multi-label classification with WAN loss
-
-## Intended Uses & Limitations
-
-### Use Cases
+### Intended uses:
 - **Website categorization**: Classifies websites into multiple sectors for general exploration or broader categorization tasks.
 - **Research**: Suitable for research on multi-sector classification or multi-label classification tasks where label dependencies are important.
 - **Content Management**: Can be used in platforms where it's important to categorize content across multiple industries or sectors.
 
-### Limitations
-- **Single Positive Label**: Trained with only the primary sector observable, potentially limiting its accuracy in predicting secondary sectors.
+### Limitations:
+- **Single Positive Label**: Only primary sector labels are observable during training, which might limit performance when predicting secondary sectors.
 - **Flexible mode**: Focuses on recall, which may lead to over-predicting some sectors in websites with ambiguous content.
-- **Data Imbalance**: Some sectors are underrepresented in the dataset, which may affect model performance on certain sectors.
+- **Dataset imbalance**: Some sectors are underrepresented, which may affect performance in predicting those categories.
 
-## Dataset
+## How to use
+
+To use this model with Hugging Face's transformers library:
 
-- **Dataset name**: WebSector Corpus
-- **Training set size**: 109,476 websites
-- **Sectors**:
-  1. Finance, Marketing & HR
-  2. Information Technology & Electronics
-  3. Consumer & Supply Chain
-  4. Civil, Mechanical & Electrical
-  5. Medical
-  6. Sports, Media & Entertainment
-  7. Education
-  8. Government, Defense & Legal
-  9. Travel, Food & Hospitality
-  10. Non-Profit
-- **Labeling**: Each website is labeled with its primary sector, derived from self-declared industry categories.
+```python
+from transformers import pipeline
 
-## Evaluation Metrics
+classifier = pipeline("text-classification", model="Shahriar/WebSector-Flexible")
+result = classifier("Your website content\URL here")
+print(result)
+```
 
-- **Top-1 Recall**: Measures the model's ability to correctly identify the primary sector as the most likely predicted sector.
-- **Top-3 Recall**: Evaluates the model's capacity to have the true sector within the top three predicted labels.
-- **Recall**: Assesses the model's ability to predict all relevant sectors, not just the primary one.
+This will return the predicted sectors of the website based on its content.
 
-The flexible mode maximizes recall, making it ideal for capturing as many relevant sectors as possible, though it may compromise precision.
+## Dataset
 
-## Training Process
+The model was trained on the **WebSector Corpus**, which consists of 254,702 websites categorized into 10 broad sectors. The training set contains 109,476 websites. The dataset is split as follows:
+- **Training set**: 109,476 websites
+- **Validation set**: 27,370 websites
+- **Test set**: 58,649 websites
+
+The 10 sectors used for classification are:
+- Finance, Marketing & HR
+- Information Technology & Electronics
+- Consumer & Supply Chain
+- Civil, Mechanical & Electrical
+- Medical
+- Sports, Media & Entertainment
+- Education
+- Government, Defense & Legal
+- Travel, Food & Hospitality
+- Non-Profit
+
+## Training Procedure
 
 ### Hyperparameters:
-- Number of epochs: 7
-- Batch size: 8
-- Learning rate: 5×10^-6
-- Weight decay: 0.1
-- LoRA rank: 128
-- LoRA alpha: 512
-- Dropout rate: 0.1
+- **Number of epochs**: 7
+- **Batch size**: 8
+- **Learning rate**: $5 \times 10^{-6}$
+- **Weight decay**: 0.1
+- **LoRA rank**: 128
+- **LoRA alpha**: 512
+- **Dropout rate**: 0.1
 
 ### Training Setup:
-- **Hardware**: Four GPUs, including two NVIDIA RTX A5000 and two NVIDIA TITAN RTX units for parallel processing.
-- **Software**: PyTorch framework and Hugging Face Transformers library.
-- **Strategy**: Distributed training across four GPUs, with model selection based on the lowest validation loss.
+- **Hardware**: Four GPUs, including two NVIDIA RTX A5000 and two NVIDIA TITAN RTX units, were used for distributed training.
+- **Software**: The model was trained using the PyTorch framework, with the Hugging Face Transformers library for implementing transformer-based models.
+- **Strategy**: Distributed training was employed, and models were selected based on the lowest validation loss.
 
-## Model Performance
+## Evaluation
 
-- Top-1 Recall: 68%
-- Top-3 Recall: 85%
-- Recall: 86%
-- Precision: 68%
+The model was evaluated on the **WebSector Corpus** using metrics appropriate for multi-label classification:
 
-These metrics show that the flexible mode of the WebSector model is optimized for recall, allowing it to capture multiple relevant sectors while maintaining a solid precision score.
+- **Top-1 Recall**: 68%
+- **Top-3 Recall**: 85%
+- **Recall**: 86%
+- **Precision**: 68%
+
+These metrics show that the flexible mode maximizes recall, allowing it to capture multiple relevant sectors while maintaining a solid precision score.
 
 ## Ethical Considerations
 
-- **Privacy Enforcement**: This model can assist in classifying websites into sectors relevant to privacy regulations like CCPA or HIPAA.
+- **Privacy Enforcement**: The model can assist in classifying websites into sectors relevant to privacy regulations like CCPA or HIPAA.
 - **Bias**: As the model was trained on self-declared sector labels, there is potential for bias due to inaccurate or incomplete labeling.
 
 ## Citation
 
 If you use this model in your research, please cite the following paper:
 
-```
-Shahriar Shayesteh, Mukund Srinath, Lee Matheson, Florian Schaub, C. Lee Giles, and Shomir Wilson. "WebSector: A New Insight into Multi-Sector Website Classification Using Single Positive Labels". Conference acronym 'XX, June 03–05, 2018, Woodstock, NY.
+```bibtex
+@article{?,
+  title={WebSector: A New Insight into Multi-Sector Website Classification Using Single Positive Labels},
+  author={Shayesteh, Shahriar and Srinath, Mukund and Matheson, Lee and Schaub, Florian and Giles, C. Lee and Wilson, Shomir},
+  journal={?},
+  year={?},
+}
 ```