Upload README.md

README.md

@@ -0,0 +1,88 @@
+---
+license: apache-2.0
+base_model: roberta-base
+tags:
+- text-classification
+- question-answering
+- roberta
+- pytorch
+- transformers
+language:
+- en
+pipeline_tag: text-classification
+---
+
+# Cyber_Bot
+
+This is a fine-tuned RoBERTa model for question-answering classification tasks.
+
+## Model Details
+
+- **Base Model**: roberta-base
+- **Model Type**: Sequence Classification
+- **Language**: English
+- **License**: Apache 2.0
+
+## Model Information
+
+
+- **Number of Classes**: 5
+- **Classification Type**: grouped_classification
+- **Class Names**: Empty, Word, Short, Medium, Long
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained('whitedevil0089devil/Cyber_Bot')
+model = AutoModelForSequenceClassification.from_pretrained('whitedevil0089devil/Cyber_Bot')
+
+# Example usage
+question = "Your question here"
+inputs = tokenizer(question, return_tensors="pt", truncation=True, padding=True, max_length=384)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(outputs.logits, dim=-1).item()
+    confidence = predictions[0][predicted_class].item()
+
+print(f"Predicted class: {predicted_class}")
+print(f"Confidence: {confidence:.4f}")
+```
+
+## Training Details
+
+This model was fine-tuned using:
+- **Framework**: PyTorch + Transformers
+- **Optimization**: AdamW with learning rate scheduling
+- **Training Strategy**: Early stopping with validation monitoring
+- **Hardware**: Trained on Google Colab (T4 GPU)
+
+## Intended Use
+
+This model is designed for question-answering classification tasks. It can be used to:
+- Classify questions into predefined categories
+- Provide automated responses based on question classification
+- Support Q&A systems and chatbots
+
+## Limitations
+
+- Model performance depends on the similarity between training data and inference data
+- May not generalize well to domains significantly different from training data
+- Classification accuracy may vary based on question complexity and length
+
+## Citation
+
+If you use this model, please cite:
+```
+@misc{roberta-qa-model,
+  title={Fine-tuned RoBERTa for Question-Answer Classification},
+  author={Your Name},
+  year={2024},
+  url={https://huggingface.co/whitedevil0089devil/Cyber_Bot}
+}
+```