# Google Scholar Papers on Transformer Attention

**Use case:** 

Get Google Scholar results for a research topic with title, authors, publication, citation count and links.

## Input

```json
{
  "queries": [
    {
      "q": "transformer attention"
    }
  ],
  "hl": "en",
  "start": 0,
  "num": 10,
  "scisbd": 0,
  "maxResults": 10,
  "maxPages": 1
}
```

## Output

```json
{
  "title": {
    "label": "Title",
    "format": "text"
  },
  "link": {
    "label": "Link",
    "format": "link"
  },
  "snippet": {
    "label": "Snippet",
    "format": "text"
  },
  "publication_info": {
    "label": "Publication Info",
    "format": "text"
  },
  "result_id": {
    "label": "Result ID",
    "format": "text"
  }
}
```

## How it works

### 1. Sign up on Apify or log in

The Google Scholar Scraper Actor is ready to run with the inputs already set for "Google Scholar Papers on Transformer Attention". [Sign up or log in](https://console.apify.com/create-task-from-example/PO74duDNvs19m3Gx5) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Google Scholar Papers on Transformer Attention" task

The Google Scholar Scraper Actor will start running based on the input automatically. You can also change all the input fields, like research queries, language, and more, to match your specific use case.

### 3. Receive the "Google Scholar Papers on Transformer Attention" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as title, link, snippet, and more.

### 4. Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also [run the "Google Scholar Papers on Transformer Attention" task via API](https://apify.com/thescrappa/google-scholar-scraper.md).

## About this Actor

This example demonstrates how to use [Google Scholar Scraper](https://apify.com/thescrappa/google-scholar-scraper.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/thescrappa/google-scholar-scraper.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/thescrappa/google-scholar-scraper.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).