Add model card with metadata

README.md

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+---
+language:
+- en
+license: mit
+library_name: mlflow
+tags:
+- intent-classification
+- text-classification
+- mlflow
+datasets:
+- custom
+metrics:
+  loss: 1.0714781284332275
+  epoch: 2.0
+model-index:
+- name: Intent Classification Model
+  results:
+  - task:
+      type: text-classification
+      subtype: intent-classification
+    metrics:
+    - type: loss
+      value: 1.0714781284332275
+    - type: epoch
+      value: 2.0
+
+---
+
+# Intent Classification Model
+
+This is an intent classification model trained using MLflow and uploaded to the Hugging Face Hub.
+
+## Model Details
+
+- **Model Type:** Intent Classification
+- **Framework:** MLflow
+- **Run ID:** ebe2ca3ecb634a96bf1ea3f65b2f86b9
+
+## Training Details
+
+### Parameters
+```yaml
+num_epochs: '2'
+model_name: distilbert-base-uncased
+learning_rate: 5e-05
+early_stopping_patience: None
+weight_decay: '0.01'
+batch_size: '32'
+max_length: '128'
+num_labels: '3'
+
+```
+
+### Metrics
+```yaml
+loss: 1.0714781284332275
+epoch: 2.0
+
+```
+
+## Usage
+
+This model can be used to classify intents in text. It was trained using MLflow and can be loaded using the MLflow model registry.
+
+### Loading the Model
+
+```python
+import mlflow
+
+# Load the model
+model = mlflow.pyfunc.load_model("runs:/ebe2ca3ecb634a96bf1ea3f65b2f86b9/intent_model")
+
+# Make predictions
+text = "your text here"
+prediction = model.predict([{"text": text}])
+```
+
+## Additional Information
+
+For more information about using this model or the training process, please refer to the repository documentation.