Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- text-classification
+- zero-shot-classification
+language:
+- en
+tags:
+- star_trek
+- qwen
+- Qwen3Guard
+pretty_name: Star Trek Classifica
+size_categories:
+- n<1K
+---
+
+# Star Trek Guard Dataset
+
+A binary classification dataset for training guard models to identify whether user inputs are related to Star Trek or not. This dataset is designed for fine-tuning language models to act as content filters, ensuring that only Star Trek-related queries are processed by specialized Star Trek AI assistants.
+
+## Dataset Description
+
+The Star Trek Guard Dataset contains **5,000 examples** of questions and statements labeled as either:
+- **`related`**: Inputs that are relevant to Star Trek (characters, ships, episodes, concepts, etc.)
+- **`not_related`**: Inputs that are not related to Star Trek (general knowledge, other topics, etc.)
+
+### Dataset Structure
+
+Each example in the dataset follows this JSON format:
+
+```json
+{"input": "What is the role of James T. Kirk in Star Trek?", "label": "related"}
+{"input": "What is the capital of France?", "label": "not_related"}
+```
+
+### Fields
+
+- **`input`** (string): The text input/question to be classified
+- **`label`** (string): The classification label, either `"related"` or `"not_related"`
+
+## Dataset Statistics
+
+- **Total Examples**: 5,000
+- **Format**: JSONL (JSON Lines)
+- **Task**: Binary Text Classification
+- **Labels**: 
+  - `related`: Star Trek-related content
+  - `not_related`: Non-Star Trek content
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load from Hugging Face Hub
+dataset = load_dataset("your-username/star-trek-guard-dataset")
+
+# Or load from local JSONL file
+dataset = load_dataset("json", data_files="star_trek_guard_dataset.jsonl")
+```
+
+### Example Usage in Training
+
+This dataset is designed to be used with the Hugging Face Transformers library for fine-tuning sequence classification models. Here's a basic example:
+
+```python
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load dataset
+dataset = load_dataset("json", data_files="star_trek_guard_dataset.jsonl")["train"]
+
+# Map labels to IDs
+LABEL2ID = {"not_related": 0, "related": 1}
+ID2LABEL = {0: "not_related", 1: "related"}
+
+dataset = dataset.map(lambda x: {"labels": LABEL2ID[x["label"]]})
+
+# Split into train/test
+dataset = dataset.train_test_split(test_size=0.1)
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B", trust_remote_code=True)
+model = AutoModelForSequenceClassification.from_pretrained(
+    "Qwen/Qwen3-4B",
+    num_labels=2,
+    id2label=ID2LABEL,
+    label2id=LABEL2ID,
+    trust_remote_code=True
+)
+
+# Tokenize
+def tokenize_function(examples):
+    return tokenizer(
+        examples["input"],
+        truncation=True,
+        padding="max_length",
+        max_length=512,
+    )
+
+tokenized_dataset = dataset.map(
+    tokenize_function,
+    batched=True,
+    remove_columns=["input", "label"]
+)
+```
+
+For a complete training script, see the reference implementation in `train_star_trek_guard.py`.
+
+## Use Cases
+
+### 1. Content Moderation for Star Trek Chatbots
+
+This dataset enables training guard models that can filter user inputs before they reach a Star Trek-specific AI assistant. Only Star Trek-related queries are allowed through, ensuring the assistant stays on-topic.
+
+### 2. API-Based Moderation
+
+The fine-tuned model can be deployed as a moderation API endpoint:
+
+```python
+# Example API endpoint (see star_trek_api_server.py for full implementation)
+@app.route('/api/moderate', methods=['POST'])
+def moderate():
+    data = request.json
+    message = data.get('message', '')
+    
+    # Classify the message
+    inputs = tokenizer(message, return_tensors="pt", truncation=True, max_length=512)
+    outputs = model(**inputs)
+    predicted_label = ID2LABEL[outputs.logits.argmax().item()]
+    
+    # Return moderation result
+    risk_level = "Safe" if predicted_label == "related" else "Unsafe"
+    return jsonify({
+        'risk_level': risk_level,
+        'predicted_label': predicted_label,
+        'confidence': float(torch.softmax(outputs.logits, dim=-1).max())
+    })
+```
+
+### 3. Real-Time Chat Filtering
+
+The guard model can be integrated into chat interfaces to provide real-time moderation, blocking non-Star Trek queries before they're sent to the LLM. See `star_trek_chat.html` for a complete implementation example.
+
+## Model Training Recommendations
+
+Based on the reference training script, recommended hyperparameters:
+
+- **Base Model**: Qwen/Qwen3-4B
+- **Learning Rate**: 2e-4
+- **Batch Size**: 2 (with gradient accumulation of 16)
+- **Epochs**: 3
+- **Max Length**: 512 tokens
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+  - `r=16`
+  - `lora_alpha=32`
+  - `lora_dropout=0.05`
+  - Target modules: `["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]`
+
+## Dataset Examples
+
+### Related Examples
+
+```json
+{"input": "What is the role of James T. Kirk in Star Trek?", "label": "related"}
+{"input": "Who portrayed Spock in Star Trek?", "label": "related"}
+{"input": "What is the Prime Directive in Star Trek?", "label": "related"}
+{"input": "How does a warp drive work?", "label": "related"}
+{"input": "What is the 49th Rule of Acquisition?", "label": "related"}
+```
+
+### Not Related Examples
+
+```json
+{"input": "What is the capital of France?", "label": "not_related"}
+{"input": "What is 2 + 2?", "label": "not_related"}
+{"input": "Is the sifaka endangered?", "label": "not_related"}
+{"input": "When was baseball first played?", "label": "not_related"}
+{"input": "How many employees does Spotify have?", "label": "not_related"}
+```
+
+## Label Mapping
+
+The dataset uses the following label mapping for model training:
+
+- `"not_related"` → Class ID `0`
+- `"related"` → Class ID `1`
+
+In the context of content moderation:
+- **`related`** = **Safe** (Star Trek-related content, allowed)
+- **`not_related`** = **Unsafe** (Non-Star Trek content, blocked)
+
+## Citation
+
+If you use this dataset in your research or project, please cite it appropriately:
+
+```bibtex
+@dataset{star_trek_guard_dataset,
+  title={Star Trek Guard Dataset},
+  author={Your Name},
+  year={2024},
+  url={https://huggingface.co/datasets/your-username/star-trek-guard-dataset}
+}
+```
+
+## License
+
+Apache 2.0
+
+## Acknowledgments
+
+This dataset was created for training guard models to ensure Star Trek AI assistants remain focused on Star Trek-related content, improving user experience and maintaining topic relevance.
+
+## Related Resources
+
+- **Training Script**: See `train_star_trek_guard.py` for a complete fine-tuning implementation
+- **API Server**: See `star_trek_api_server.py` for deployment as a moderation API
+- **Chat Interface**: See `star_trek_chat.html` for integration into a web-based chat application