HuggingInfo

Unofficial library to retrieve information from the HuggingFace website.

Installation

Clone the repository and install the dependencies:

git clone
cd hugginginfo
npm install

Usage

const HuggingInfo = require("./hugginginfo/src/main.js");
const hf = new HuggingInfo();

Methods

getTasks()

Fetches the tasks available from the Huggingface model hub (on the left of the page at huggingface.co/models). Returns a promise that resolves to an object.

hf.getTasks().then((tasks) => {
    console.log(tasks);
});

Example response:

{
    "url": "https://huggingface.co/models",
    "tasks": {
        {
            "name": "Feature Extraction",
            "url": "/models?pipeline_tag=feature-extraction"
        },
        {
            "name": "Text-to-Image",
            "url": "/models?pipeline_tag=text-to-image"
        }
        ...
    }
}

getModelList()

Fetches the model list from the Huggingface model hub for a certain task. Parameters:

• task: The task to fetch models for. Must be filled with a valid task.
• sortBy: The field to sort by. Defaults to "downloads".
• page: The page to fetch. Defaults to 0.

Returns a promise that resolves to an object.

hf.getModelList("fill-mask").then((models) => {
    console.log(models);
});

Example response:

{
  "url": "https://huggingface.co/models?pipeline_tag=fill-mask&sort=downloads&p=0",
  "totalModels": 5738,
  "models": [
    {
      "name": "bert-base-uncased",
      "lastUpdated": "Nov 16, 2022",
      "downloads": "31.7M",
      "likes": "523"
    },
    {
      "name": "emilyalsentzer/Bio_ClinicalBERT",
      "lastUpdated": "Feb 27, 2022",
      "downloads": "12.2M",
      "likes": "70"
    },
    ...
  ],
  "nextPage": 1
}

getModelInfo()

Fetches model information from the Huggingface model hub. Parameters:

• name: The name of the model.

Returns a promise that resolves to an object.

hf.getModelInfo("bert-base-uncased").then((model) => {
    console.log(model);
});

Example response:

{
  "url": "https://huggingface.co/bert-base-uncased",
  "name": "bert-base-uncased",
  "shortDescription": "Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.",
  "downloads": 31684957,
  "likes": 523,
  "tags": [
    "Fill-Mask",
    "PyTorch",
    "TensorFlow",
    "JAX",
    "Rust",
    "Safetensors",
    "Transformers",
    "bookcorpus",
    "wikipedia",
    "English",
    "arxiv:1810.04805",
    "bert",
    "exbert",
    "AutoTrain Compatible"
  ],
  "license": "apache-2.0",
  "fillType": "[MASK]"
}

TODO

• Publish on NPM

Note

This library is not affiliated with Huggingface in any way. All the information is retrieved from the Huggingface website. If you want to use this library in a project, please make sure to follow the Huggingface Terms of Service.