# Hugging Face {% hint style="info" %} **Supported labeling types**: Span labeling**,** row labeling. {% endhint %} Datasaur integrates directly with Hugging Face, providing access to their 10k+ models. After choosing Hugging Face as the option, you can go to [Hugging Face](https://huggingface.co/models) website and choose the available models. You can also use your own private models hosted on Hugging Face. ## Span labeling For span labeling, enter the model name or provide an endpoint URL if using a self-hosted model. When using your own endpoint, a model name or API token is not required. You can also set a confidence score to adjust the prediction threshold.
Image of ML Assisted with Hugging Face for Span Based
## Row labeling In row labeling, select a column as the input in the **Target text** field and choose the target question as the output. To get started, enter either the **model name** or the **Dedicated Inference Endpoint URL**, along with your **API token**.
Image of ML Assisted with Hugging Face for Row Based
When choosing a model for label prediction, use a text classification model. The model should return a list of dictionaries (or objects) where each object contains all predictions (positive, negative, neutral), like this: ``` [ [ { label: "positive", score: 0.8 }, { label: "neutral", score: 0.15 }, { label: "negative", score: 0.05 } ], [ { label: "negative", score: 0.6 }, { label: "neutral", score: 0.3 }, { label: "positive", score: 0.1 } ] ] ``` or just a single list/array that contains objects of single prediction (the highest score), like this: ``` [ { label: "positive", score: 0.8 }, { label: "negative", score: 0.6 } ] ``` There's an option for **Faster prediction speed**, which improves performance by processing entire rows at once. Note that this action cannot be undone. You can also adjust the **Confidence score** to set the prediction threshold. Clicking **Predict labels** will automatically apply labels to the document based on the loaded model. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.datasaur.ai/assisted-labeling/ml-assisted-labeling/ml-assisted-using-huggingface.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.