| 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. | |