---
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.