Update README.md

README.md

@@ -1,199 +1,315 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- text-classification
+- distilbert
+- command-classification
+- intent-detection
+- nlp
+language:
+- en
+license: apache-2.0
+metrics:
+- accuracy
+- f1
+base_model: distilbert-base-uncased
+pipeline_tag: text-classification
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# DistilBERT Command Classifier
 
+A fine-tuned DistilBERT model for classifying user commands and questions with high accuracy, including handling of typos and variations.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model is a fine-tuned version of `distilbert-base-uncased` specifically trained to classify various command types from user input. It's designed to handle natural language commands with typos, variations in phrasing, and different command intents.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** jhonacmarvik
+- **Model type:** Text Classification (Sequence Classification)
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** distilbert-base-uncased
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Base Model:** [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)
+- **Framework:** PyTorch + Transformers
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+This model can be directly used for:
+- **Command intent classification** - Identify what action the user wants to perform
+- **Voice assistant routing** - Route commands to appropriate handlers
+- **Natural language interface control** - Control systems through natural language
+- **Question vs Command detection** - Distinguish between questions and actionable commands
+
+### Example Usage
+
+```python
+from transformers import pipeline
+
+# Load the classifier
+classifier = pipeline(
+    "text-classification",
+    model="jhonacmarvik/distilbert-command-classifier",
+    top_k=3
+)
+
+# Single prediction
+result = classifier("Turn on all work lights")
+print(result)
+# Output: [
+#   {'label': 'turn_on_lights', 'score': 0.9234},
+#   {'label': 'increase_brightness', 'score': 0.0543},
+#   {'label': 'turn_off_lights', 'score': 0.0123}
+# ]
+
+# Batch prediction
+commands = [
+    "Turn on all work lights",
+    "Decrease the brightness",
+    "What's the temperature?"
+]
+results = classifier(commands)
+```
+
+### Alternative Usage (Manual)
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    "jhonacmarvik/distilbert-command-classifier"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "jhonacmarvik/distilbert-command-classifier"
+)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# Tokenize
+text = "Turn on all work lights"
+tokens = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+tokens = {k: v.to(device) for k, v in tokens.items()}
+
+# Predict
+with torch.no_grad():
+    outputs = model(**tokens)
+    probs = torch.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probs, dim=-1)
+    
+print(f"Predicted: {model.config.id2label[predicted_class.item()]}")
+print(f"Confidence: {probs[0][predicted_class].item():.4f}")
+```
+
+### Downstream Use
+
+Can be integrated into:
+- Smart home systems
+- Voice assistants
+- Chatbots and conversational AI
+- IoT device control interfaces
+- Natural language command parsers
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is NOT suitable for:
+- Commands outside its training vocabulary
+- Languages other than English
+- Sentiment analysis or emotion detection
+- General text classification tasks unrelated to commands
+- Safety-critical applications without human oversight
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- **Vocabulary Limitation:** Model is trained on specific command types and may not generalize to completely novel command categories
+- **Typo Handling:** While trained on variations with typos, extreme misspellings may reduce accuracy
+- **Context Awareness:** Model processes single utterances and doesn't maintain conversation context
+- **Language:** Only supports English language commands
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- Implement confidence thresholds (e.g., > 0.7) before executing commands
+- Provide fallback mechanisms for low-confidence predictions
+- Add human-in-the-loop for critical operations
+- Monitor model performance on production data and retrain periodically
+- Test thoroughly with your specific use case before deployment
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+- **Dataset:** Custom dataset of command variations with intentional typos and paraphrases
+- **Size:** Multiple variations per command class
+- **Format:** CSV with text variations and corresponding labels
+- **Split:** 80% training, 20% validation (stratified)
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+#### Preprocessing
 
+- Text converted to lowercase
+- Tokenization using DistilBERT tokenizer
+- Maximum sequence length: 128 tokens
+- Padding and truncation applied
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
+- **Training regime:** FP32
+- **Optimizer:** AdamW
+- **Learning rate:** 2e-5
+- **Warmup steps:** 100
+- **Weight decay:** 0.01
+- **Batch size:** 16 (per device)
+- **Number of epochs:** 10
+- **Early stopping patience:** 3 epochs
+- **Evaluation strategy:** Per epoch
+- **Best model selection:** Based on eval_loss
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Hardware & Software
 
-[More Information Needed]
+- **Framework:** PyTorch + Transformers (Hugging Face)
+- **Base model:** distilbert-base-uncased
+- **Hardware:** GPU (CUDA-enabled) or CPU compatible
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
+### Metrics
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+The model was evaluated using:
+- **Accuracy:** Overall classification accuracy
+- **F1 Score:** Per-class and macro-averaged F1
+- **Precision & Recall:** Per-class metrics
+- **Confusion Matrix:** Visual representation of classification performance
+- **ROC-AUC:** Per-class ROC curves
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+Model achieves high accuracy on the validation set with strong performance across all command classes. Detailed metrics are available in the training outputs.
+
+*Note: Specific metrics depend on your final training results. Update with actual values after training.*
+
+## How to Get Started
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Quick Start
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification", 
+    model="jhonacmarvik/distilbert-command-classifier"
+)
+
+result = classifier("Turn on the lights")
+print(result)
+```
+
+### Production Deployment
+
+For production use with custom loading pattern:
+
+```python
+import os
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+class CommandClassifier:
+    def __init__(self):
+        model_path = "jhonacmarvik/distilbert-command-classifier"
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
+        self.model.to(self.device)
+        self.model.eval()
+    
+    def predict(self, text: str, top_k: int = 3):
+        tokens = self.tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+        tokens = {k: v.to(self.device) for k, v in tokens.items()}
+        
+        with torch.no_grad():
+            logits = self.model(**tokens).logits
+            probs = torch.softmax(logits, dim=-1)
+            top_probs, top_indices = torch.topk(probs, k=top_k)
+        
+        results = []
+        for prob, idx in zip(top_probs[0], top_indices[0]):
+            results.append({
+                "label": self.model.config.id2label[idx.item()],
+                "score": float(prob.item())
+            })
+        return results
+
+# Usage
+classifier = CommandClassifier()
+result = classifier.predict("Turn on lights", top_k=3)
+```
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+Training a single model on standard GPU hardware has minimal environmental impact compared to large language models. This model uses a lightweight DistilBERT architecture which is significantly more efficient than full BERT models.
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Hardware Type:** GPU (CUDA-enabled)
+- **Compute Region:** [Your region]
+- **Carbon Impact:** Minimal due to efficient architecture
 
-## Technical Specifications [optional]
+## Technical Specifications
 
-### Model Architecture and Objective
+### Model Architecture
 
-[More Information Needed]
+- **Base Architecture:** DistilBERT (6-layer, 768-hidden, 12-heads)
+- **Parameters:** ~66M parameters
+- **Classification Head:** Linear layer for multi-class classification
+- **Dropout:** 0.1 (default DistilBERT configuration)
+- **Activation:** GELU
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- Compatible with CPU and GPU (CUDA)
+- Recommended: GPU with 4GB+ VRAM for faster inference
+- Works on CPU for low-volume applications
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- Python 3.8+
+- PyTorch 2.0+
+- Transformers 4.30+
+- CUDA 11.0+ (for GPU acceleration)
 
-[More Information Needed]
+## Citation
 
-## More Information [optional]
+If you use this model in your research or application, please cite:
 
-[More Information Needed]
+```bibtex
+@misc{distilbert-command-classifier,
+  author = {jhonacmarvik},
+  title = {DistilBERT Command Classifier},
+  year = {2024},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/jhonacmarvik/distilbert-command-classifier}}
+}
+```
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+jhonacmarvik
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or issues, please open an issue in the model repository or contact through HuggingFace.