Update README.md

README.md

@@ -1,11 +1,21 @@
 ---
-library_name: transformers
-tags: []
+tags:
+- pet_Health
+- veterinary
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- f1
+base_model:
+- havocy28/VetBERTDx
+pipeline_tag: text-classification
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model classifies pet health symptoms from text descriptions into predefined health conditions, fine-tuned on VetBERTDx.
 
 
 
@@ -13,187 +23,358 @@ tags: []
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+Fine-tuned VetBERTDx for sequence classification.
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** Fatemeh Dastak
 - **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 type:** Fine-tuned VetBERTDx for sequence classification
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model [optional]:** havocy28/VetBERTDx
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/fdastak/model_classification
+- **Dataset:** [Pet Health Symptoms Dataset](https://www.kaggle.com/datasets/yyzz1010/pet-health-symptoms-dataset)
 
 ## 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
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+model = AutoModelForSequenceClassification.from_pretrained("fdastak/model_classification")
+tokenizer = AutoTokenizer.from_pretrained("fdastak/model_classification")
+```
 
-[More Information Needed]
+### Out-of-Scope Use
+- Not for actual medical diagnosis
+- Not a replacement for veterinary consultation
+- Not suitable for emergency medical decisions
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This model can be integrated into:
 
-[More Information Needed]
+- Veterinary triage systems
+- Pet health monitoring applications
+- Symptom screening tools
+- Educational veterinary platforms
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+This model should NOT be used for:
 
-[More Information Needed]
+- Direct medical diagnosis
+- Emergency medical decisions
+- Replacement of veterinary consultation
+- Legal or insurance decisions
+- Automated treatment recommendation
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## Technical Limitations
 
-[More Information Needed]
+- Limited to 512 token input length
+- CPU-only training constraints
+- Early stopping at 301 steps
+- Batch size limitations (8 training, 20 evaluation)
+- Specific to owner-reported symptoms
 
-### Recommendations
+## Data Biases
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Training data from owner observations only
+- English language only
+- Limited to common pet conditions
+- Potential reporting biases in symptoms
+- Class imbalance considerations
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+### Risk
 
-## How to Get Started with the Model
+-Misinterpretation of medical conditions
+-Over-reliance on automated classification
+-Delayed professional consultation
+-False confidence in predictions
+-Language and cultural biases
 
-Use the code below to get started with the model.
+### Recommendations
 
-[More Information Needed]
+## Best Practices
 
-## Training Details
+- Always verify predictions with professionals
+- Use as screening tool only
+- Monitor prediction confidence scores
+- Implement user warnings
+- Regular model evaluation
 
-### Training Data
+## How to Get Started with the Model
 
-<!-- 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. -->
+# Load required libraries
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch.nn.functional as F
 
-[More Information Needed]
+# Load model and tokenizer
+repo_id = "fdastak/model_classification"
+model = AutoModelForSequenceClassification.from_pretrained(repo_id)
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
 
-### Training Procedure
+# Example usage
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+def classify_symptoms(text: str):
+    # Preprocess and tokenize
+    inputs = tokenizer(
+        text,
+        truncation=True,
+        padding=True,
+        max_length=512,
+        return_tensors="pt"
+    )
+    
 
-#### Preprocessing [optional]
+## Training Details
 
-[More Information Needed]
+### Training Data
+- Source: Pet Health Symptoms Dataset (Kaggle)
+- Split: 80% training, 20% validation
+- Preprocessing: Text lowercasing, label encoding
 
+### Training Procedure
 
 #### Training Hyperparameters
+- Epochs: 5
+- Train batch size: 8
+- Eval batch size: 20
+- Learning rate: 2e-5
+- Scheduler: Linear with warmup
+- Warmup ratio: 0.1
+- Early stopping: At step 301
+- Maximum sequence length: 512
 
-- **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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Evaluation
 
-[More Information Needed]
+#### Metrics
+- Accuracy
+- Precision (weighted)
+- Recall (weighted)
+- F1-score (weighted)
+- 
+#### Speeds, Sizes, Times
+- **Training Duration**: ~1 hour
+- **Steps**: 301 (with early stopping)
+- **Checkpoint Frequency**: Every 50 steps
+- **Batch Processing**:
+  - Training: 8 samples/batch
+  - Evaluation: 20 samples/batch
+- **Model Storage**: Local checkpoints in './model_classification'
 
 ## 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. -->
-
-[More Information Needed]
+- **Source**: [Pet Health Symptoms Dataset](https://www.kaggle.com/datasets/yyzz1010/pet-health-symptoms-dataset)
+- **Split**: 20% of data (validation set)
+- **Format**: Text descriptions with condition labels
+- **Preprocessing**: Text lowercasing, label encoding
 
 #### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+- **Record Types**: Owner observations
+- **Text Length**: Maximum 512 tokens
+- **Language**: English
+- **Conditions**: Multiple pet health conditions
+- **Data Balance**: Stratified split for class distribution
 
 #### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Accuracy**: Overall classification accuracy
+- **Precision (weighted)**: Measure of exactness
+- **Recall (weighted)**: Measure of completeness
+- **F1-score (weighted)**: Harmonic mean of precision and recall
+- **Confusion Matrix**: Class-wise performance visualization
 
 ### Results
 
-[More Information Needed]
+#### Performance Summary
+- Overall Accuracy: 89%
+- Average F1-Score: 0.89
+- Class-wise Performance:
+  - Class 0: Highest precision (0.97) and F1-score (0.95)
+  - Class 1: Perfect recall (1.00)
+  - Class 2: Balanced performance (0.93 across metrics)
+  - Classes 3 & 4: Similar performance (~0.82-0.83 F1-score)
+
+#### Key Metrics
+- **Precision (weighted)**: 0.89
+- **Recall (weighted)**: 0.89
+- **F1-score (weighted)**: 0.89
+- **Support**: 200 validation samples (40 per class)
 
 #### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+- Model shows balanced performance across classes
+- Early stopping at step 301 prevents overfitting
+- Validation performed every 50 steps
+- Best model selected based on eval_loss
+- Confusion matrix shows class-wise performance
+
+
+## Model Examination 
+
+### Validation Results
+The model's performance was examined using several evaluation methods:
+
+1. **Classification Metrics**
+- Computed using sklearn's classification_report
+- Includes precision, recall, and F1-score
+- Evaluated on validation dataset
+- Weighted averages to handle class imbalance
+
+2. **Confusion Matrix Analysis**
+```python
+# Visualization code
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+import matplotlib.pyplot as plt
+
+model.eval()
+with torch.no_grad():
+    # Prediction collection
+    true_labels = []
+    pred_labels = []
+    pred_scores = []
+    # ...evaluation logic
+```
+
+3. **Prediction Confidence**
+- Softmax probabilities for class predictions
+- Confidence scores tracked for each prediction
+- Score distribution analysis for reliability
+
+4. **Early Stopping Analysis**
+- Training stopped at step 301
+- Monitored eval_loss for best model selection
+- Used custom StopAtStepCallback for controlled training
+
+### Model Interpretability
+- Base model: VetBERTDx (domain-specific veterinary BERT)
+- Fine-tuned for pet symptom classification
+- Uses attention mechanisms for text understanding
+- Maximum sequence length: 512 tokens
+
+### Limitations
+- CPU-only training might affect model capacity
+- Limited to predefined condition categories
+- Performance varies by symptom complexity
+- Early stopping may affect final performance
 
 ## 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:** CPU (Personal Computer)
+- **Hours used:** ~2 hours (301 steps with early stopping)
+- **Cloud Provider:** None (Local training)
+- **Compute Region:** USA (Colorado)
+- **Power Mix:** Rocky Mountain Power Grid
+- **Training Configuration:**
+  - 301 steps with early stopping
+  - CPU-based training
+  - Batch size: 8 samples
+  - Epochs: 5
+  - Local machine execution
+
+Environmental considerations:
+- Used CPU instead of GPU for lower power consumption
+- Implemented early stopping at step 301
+- Leveraged pre-trained model (VetBERTDx)
+- Local training to minimize data center impact
+- Efficient batch size selection
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- Base model: VetBERTDx
+- Task: Sequence classification
+- Input: Text descriptions of pet symptoms
+- Output: Classification among health conditions
 
 ### Compute Infrastructure
 
-[More Information Needed]
+- Framework: PyTorch
+- Training device: GPU
+- Python dependencies:
+  - transformers
+  - torch
+  - numpy
+  - scikit-learn
 
 #### Hardware
 
-[More Information Needed]
+The model was trained using:
+- Training Device: CPU
+- Batch Configuration:
+  - Training batch size: 8
+  - Evaluation batch size: 20
+- Training Steps: Limited to 301 (early stopping)
+- Local Storage: Required for model checkpoints in './model_classification'
 
 #### Software
 
-[More Information Needed]
-
+Training environment specifications:
+- Python 3.11
+- Core Libraries:
+  ```python
+  torch>=2.0.0
+  transformers>=4.30.0
+  numpy>=1.24.0
+  pandas>=1.5.0
+  scikit-learn>=1.0.0
+  sentence-transformers>=2.2.0
+  ```
+- Training Components:
+  - Framework: 🤗 Transformers
+  - Base Model: havocy28/VetBERTDx
+  - Tokenizer: AutoTokenizer
+  - Model Class: AutoModelForSequenceClassification
+  - Training API: Transformers Trainer with custom callbacks
+- Logging: Python's built-in logging module
+  
 ## 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. -->
+If you use this model in your research, please cite it using the following:
 
 **BibTeX:**
-
-[More Information Needed]
+```bibtex
+@misc{dastak2024pethealthclassifier,
+    title={Pet Health Symptoms Classification Model},
+    author={Dastak, Fatemeh},
+    year={2024},
+    publisher={Hugging Face},
+    howpublished={\url{https://huggingface.co/fdastak/model_classification}},
+    note={Based on VetBERTDx by Havocy28},
+    keywords={veterinary-nlp, text-classification, pet-health}
+}
+```
 
 **APA:**
+```
+Dastak, F. (2025). Pet Health Symptoms Classification Model [Machine learning model]. Hugging Face Model Hub. https://huggingface.co/fdastak/model_classification
+```
+
+Please also cite the base model:
+```
+@misc{havocy282023vetbertdx,
+    title={VetBERTDx: A Domain-Specific Language Model for Veterinary Medicine},
+    author={Havocy28},
+    year={2023},
+    publisher={Hugging Face},
+    howpublished={\url{https://huggingface.co/havocy28/VetBERTDx}}
+}
+```
 
-[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]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
 
 ## Model Card Contact
 
-[More Information Needed]
+Author: Fatemeh Dastak
+Repository: https://huggingface.co/fdastak/model_classification