| --- |
| license: bsd-3-clause-clear |
| library_name: transformers |
| base_model: FacebookAI/roberta-base |
| tags: |
| - text-classification |
| - roberta |
| - question-classification |
| --- |
| |
| # RoBERTa Question-Type Classifier 2.0 |
|
|
| This repository contains a fine-tuned RoBERTa model for classifying interviewer utterances into six question-type categories. |
|
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| Version 2.0 predicts six categories: |
|
|
| | Code | Question type | |
| |---|---| |
| | 0 | Non-question | |
| | 1 | Option-posing question (yes-no and forced-choice) | |
| | 2 | Wh-question (directive) | |
| | 3 | Invitation | |
| | 4 | Tag question | |
| | 5 | Do you know/remember question | |
|
|
| ## Access |
|
|
| This model uses gated access. Before downloading the model files, sign in to Hugging Face and accept the access conditions for this repository. |
|
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| Access requests are automatically approved. |
|
|
| ## Getting the Model |
|
|
| After receiving access, you need to download the model files and notebook to your computer. |
|
|
| ### Option 1: Download from Hugging Face |
|
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| Open the **Files and versions** tab and use the download button beside each file to download the repository files. |
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| Keep the downloaded files together in one folder. |
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| The folder should contain: |
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| - `roberta_question_type_predictions_public.ipynb` |
| - `config.json` |
| - `model.safetensors` |
| - `vocab.json` |
| - `merges.txt` |
| - `special_tokens_map.json` |
| - `tokenizer_config.json` |
|
|
| Then open the folder in VS Code or another Jupyter-compatible environment and open: |
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| `roberta_question_type_predictions_public.ipynb` |
|
|
| ### Option 2: Download with the Hugging Face CLI |
|
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| If you have the Hugging Face CLI installed and are signed in, you can download the complete repository with: |
|
|
| ```bash |
| hf download USCchildinterviewinglab/roberta-question-type-classifier-2.0 --local-dir roberta-question-type-classifier-2.0 |
| ``` |
|
|
| ### Option 3: Clone with Git |
|
|
| If you are familiar with Git, you can instead clone the repository: |
|
|
| ```bash |
| git clone https://huggingface.co/USCchildinterviewinglab/roberta-question-type-classifier-2.0 |
| ``` |
|
|
| Cloning is optional. You do not need to use Git in order to run the classifier. |
|
|
| ## Requirements |
|
|
| - Python 3 |
| - A Jupyter-compatible environment, such as VS Code with the Python and Jupyter extensions |
| - A Hugging Face account with access to this model in order to download the repository |
|
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| The notebook installs the required Python packages for you. |
|
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| ## Input Format |
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| Your input must be a CSV file containing a column named exactly: |
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| `Question` |
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| Other columns are allowed and will be preserved in the output. |
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| Example: |
|
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| | Question | |
| |---| |
| | Tell me everything that happened. | |
| | Did you see him? | |
| | Where did you go? | |
|
|
| ## Running the Notebook |
|
|
| 1. Download or clone this repository. |
| 2. Open the repository folder in VS Code or another Jupyter-compatible environment. |
| 3. Open `roberta_question_type_predictions_public.ipynb`. |
| 4. If prompted to select a kernel: |
| - Click **Select Kernel**. |
| - Choose **Python Environments...**. |
| - Select a Python 3 environment. |
|
|
| If you are using VS Code, make sure the Python and Jupyter extensions are installed. |
| 5. Place a copy of your CSV file in the same repository folder as the notebook. |
| 6. Make sure the CSV contains a column named exactly `Question`. |
| 7. If your file is not named `input.csv`, change `input_file` in Section 2 of the notebook to match your filename. |
| 8. Run the notebook cells from top to bottom. |
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| The notebook does not overwrite the original file. |
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| By default, predictions are written to: |
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| `input_with_question_type_predictions.csv` |
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| Two columns are added: |
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| - `question_type_code`: numeric prediction from 0–5 |
| - `question_type_label`: corresponding question-type label |
|
|
| ## Optional: Load the Model Directly with Transformers |
|
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| Developers can also load the model directly from Hugging Face using the Transformers library instead of using the included notebook. |
|
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| Because this model uses gated access, the Python environment must be authenticated with a Hugging Face account that has access to the repository. |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| model_id = "USCchildinterviewinglab/roberta-question-type-classifier-2.0" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForSequenceClassification.from_pretrained(model_id) |
| ``` |
|
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| This option is intended for users who want to integrate the classifier into their own Python code or workflow. |
|
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| ## Citation |
|
|
| Citation information will be added upon publication. |
