Update README.md

README.md

@@ -1,199 +1,305 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- text-classification
+- emotion-detection
+- sentiment-analysis
+- distilbert
+language:
+- en
+license: apache-2.0
+base_model: distilbert-base-uncased
+pipeline_tag: text-classification
+metrics:
+- accuracy
+- f1
 ---
 
-# Model Card for Model ID
+# DistilBERT Emotion Classifier
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) for multi-class emotion classification. The model classifies text into different emotional categories, enabling applications in sentiment analysis, customer feedback analysis, and social media monitoring.
 
+**Developed by:** Sathwik3
 
-## Model Details
+**Model type:** Text Classification (Emotion Detection)
 
-### Model Description
+**Language(s):** English
 
-<!-- Provide a longer summary of what this model is. -->
+**License:** Apache 2.0
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+**Base model:** [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)
 
-- **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]
+## Model Details
 
-### Model Sources [optional]
+### Architecture
 
-<!-- Provide the basic links for the model. -->
+The model is based on DistilBERT, a distilled version of BERT that retains 97% of BERT's language understanding while being 40% smaller and 60% faster. The architecture consists of:
+- 6 transformer layers
+- 768 hidden dimensions
+- 12 attention heads
+- ~66M parameters
+- Classification head for emotion prediction
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Training Objective
 
-## Uses
+The model was fine-tuned using cross-entropy loss for multi-class classification, optimizing for accurate emotion categorization across multiple emotional states.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Intended Uses
 
 ### 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]
+The model can be directly used for:
+- **Emotion detection** in text documents
+- **Sentiment analysis** of customer reviews and feedback
+- **Social media monitoring** to understand emotional tone
+- **Content moderation** based on emotional content
+- **Mental health applications** for emotion tracking in journals
+- **Chatbot enhancement** for emotion-aware responses
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+This model can be integrated into larger systems for:
+- Customer service platforms for automated response routing
+- Market research tools for analyzing consumer sentiment
+- Educational platforms for emotional intelligence training
+- Healthcare applications for mental wellness monitoring
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The model should **not** be used for:
+- Clinical diagnosis or medical decision-making
+- Making critical decisions about individuals without human oversight
+- Applications where misclassification could cause harm
+- Languages other than English (without additional fine-tuning)
+- Real-time crisis intervention or emergency response
 
-[More Information Needed]
+## Limitations and Bias
 
-## Bias, Risks, and Limitations
+### Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+- **Language limitation:** The model is trained primarily on English text and may not perform well on other languages or code-switched text
+- **Context sensitivity:** Short texts or texts lacking context may be misclassified
+- **Domain specificity:** Performance may vary across different domains (e.g., formal vs. informal text)
+- **Sarcasm and irony:** The model may struggle with non-literal expressions
+- **Cultural nuances:** Emotion expression varies across cultures, which may affect performance
 
-[More Information Needed]
+### Bias Considerations
 
-### Recommendations
+- The model's predictions may reflect biases present in the training data
+- Emotion categories may not universally apply across all cultures and contexts
+- Performance may vary across demographic groups depending on training data representation
+- Users should validate model outputs, especially in sensitive applications
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+### Recommendations
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Always review model predictions in high-stakes applications
+- Use the model as a decision support tool, not a sole decision-maker
+- Evaluate performance on your specific use case before deployment
+- Monitor for bias and fairness issues in production
+- Provide clear communication to end users about the model's capabilities and limitations
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+Use the code below to get started with the model:
 
-## Training Details
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-### Training Data
+# Load model and tokenizer
+model_name = "Sathwik3/distilbert-emotion-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-<!-- 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. -->
+# Example text
+text = "I am so happy and excited about this amazing opportunity!"
 
-[More Information Needed]
+# Tokenize and predict
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1).item()
 
-### Training Procedure
+print(f"Predicted emotion class: {predicted_class}")
+print(f"Confidence scores: {predictions}")
+```
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+For pipeline usage:
 
-#### Preprocessing [optional]
+```python
+from transformers import pipeline
 
-[More Information Needed]
+# Create emotion classification pipeline
+emotion_classifier = pipeline("text-classification", model="Sathwik3/distilbert-emotion-classifier")
 
+# Classify emotion
+result = emotion_classifier("I am so happy and excited about this amazing opportunity!")
+print(result)
+```
 
-#### Training Hyperparameters
+## Training Details
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Training Data
 
-#### Speeds, Sizes, Times [optional]
+The model was fine-tuned on an emotion classification dataset. Specific dataset details:
+- **Dataset:** [Dataset name and link - placeholder for specific information]
+- **Size:** [Number of training examples - placeholder]
+- **Emotion categories:** [List of emotion labels - placeholder]
+- **Data split:** [Train/validation/test split information - placeholder]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Training Procedure
 
-[More Information Needed]
+#### Preprocessing
 
-## Evaluation
+- Text tokenization using DistilBERT tokenizer
+- Maximum sequence length: 512 tokens
+- Truncation and padding applied as needed
+- Text normalization: [specific preprocessing steps - placeholder]
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+#### Training Hyperparameters
 
-### Testing Data, Factors & Metrics
+- **Training regime:** Mixed precision (fp16) [placeholder - adjust if different]
+- **Optimizer:** AdamW
+- **Learning rate:** [e.g., 2e-5 - placeholder]
+- **Batch size:** [e.g., 16 or 32 - placeholder]
+- **Number of epochs:** [e.g., 3-5 - placeholder]
+- **Weight decay:** [e.g., 0.01 - placeholder]
+- **Warmup steps:** [placeholder]
+- **Scheduler:** [e.g., Linear with warmup - placeholder]
 
-#### Testing Data
+#### Training Infrastructure
 
-<!-- This should link to a Dataset Card if possible. -->
+- **Hardware:** [GPU type, e.g., NVIDIA Tesla V100 - placeholder]
+- **Training time:** [Approximate duration - placeholder]
+- **Framework:** PyTorch with Hugging Face Transformers
 
-[More Information Needed]
+## Evaluation
 
-#### Factors
+### Testing Data & Metrics
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+#### Testing Data
 
-[More Information Needed]
+- **Test set:** [Description of test data - placeholder]
+- **Test set size:** [Number of examples - placeholder]
+- **Distribution:** [Class distribution information - placeholder]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+The model's performance is evaluated using:
+- **Accuracy:** Overall classification accuracy
+- **F1 Score:** Macro and weighted F1 scores for balanced evaluation
+- **Precision:** Per-class and average precision
+- **Recall:** Per-class and average recall
+- **Confusion Matrix:** For detailed error analysis
 
 ### Results
 
-[More Information Needed]
+#### Overall Performance
 
-#### Summary
+| Metric | Value |
+|--------|-------|
+| Accuracy | [e.g., 0.XX - placeholder] |
+| Macro F1 | [e.g., 0.XX - placeholder] |
+| Weighted F1 | [e.g., 0.XX - placeholder] |
+| Macro Precision | [e.g., 0.XX - placeholder] |
+| Macro Recall | [e.g., 0.XX - placeholder] |
 
+#### Per-Class Performance
 
+[Placeholder for per-class metrics table]
 
-## Model Examination [optional]
+| Emotion | Precision | Recall | F1-Score | Support |
+|---------|-----------|--------|----------|----------|
+| [Class 1] | [0.XX] | [0.XX] | [0.XX] | [N] |
+| [Class 2] | [0.XX] | [0.XX] | [0.XX] | [N] |
+| ... | ... | ... | ... | ... |
 
-<!-- Relevant interpretability work for the model goes here -->
+### Summary
 
-[More Information Needed]
+The model demonstrates strong performance on emotion classification tasks, with particular strengths in [specific aspects - placeholder]. Areas for potential improvement include [specific areas - placeholder].
 
 ## 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).
 
-- **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:** [e.g., NVIDIA Tesla V100 - placeholder]
+- **Hours used:** [placeholder]
+- **Cloud Provider:** [e.g., AWS, GCP, Azure, or on-premises - placeholder]
+- **Compute Region:** [e.g., us-east-1 - placeholder]
+- **Carbon Emitted:** [e.g., XX kg CO2eq - placeholder]
 
-## Technical Specifications [optional]
+## Technical Specifications
 
-### Model Architecture and Objective
+### Model Architecture
 
-[More Information Needed]
+- **Base Model:** DistilBERT (distilbert-base-uncased)
+- **Model Size:** ~66M parameters (base) + classification head
+- **Layers:** 6 transformer layers
+- **Hidden Size:** 768
+- **Attention Heads:** 12
+- **Intermediate Size:** 3072
+- **Max Sequence Length:** 512 tokens
+- **Vocabulary Size:** 30,522 tokens
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+[Placeholder for specific hardware information - e.g., GPU type, CPU, memory]
 
 #### Software
 
-[More Information Needed]
+- **Framework:** PyTorch
+- **Library:** Hugging Face Transformers
+- **Python Version:** [e.g., 3.8+ - placeholder]
+- **Key Dependencies:**
+  - transformers
+  - torch
+  - tokenizers
+  - datasets (if applicable)
 
-## Citation [optional]
+## Citation
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+If you use this model in your research or applications, please cite:
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@misc{sathwik3-distilbert-emotion,
+  author = {Sathwik3},
+  title = {DistilBERT Emotion Classifier},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/Sathwik3/distilbert-emotion-classifier}}
+}
+```
+
+Please also cite the original DistilBERT paper:
+
+```bibtex
+@article{sanh2019distilbert,
+  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
+  author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
+  journal={arXiv preprint arXiv:1910.01108},
+  year={2019}
+}
+```
 
 **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. -->
-
-[More Information Needed]
+Sathwik3. (2024). *DistilBERT Emotion Classifier*. Hugging Face. https://huggingface.co/Sathwik3/distilbert-emotion-classifier
 
-## More Information [optional]
+## Model Card Authors
 
-[More Information Needed]
+Sathwik3
 
-## Model Card Authors [optional]
+## Model Card Contact
 
-[More Information Needed]
+For questions or feedback about this model, please open an issue in the model's repository or contact via Hugging Face.
 
-## Model Card Contact
+---
 
-[More Information Needed]
+*This model card follows the guidelines from [Mitchell et al. (2019)](https://arxiv.org/abs/1810.03993) and the Hugging Face Model Card template.*