Instructions to use serpapi/bert-base-local-results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use serpapi/bert-base-local-results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="serpapi/bert-base-local-results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("serpapi/bert-base-local-results") model = AutoModelForSequenceClassification.from_pretrained("serpapi/bert-base-local-results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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pipeline_tag: text-classification
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widget:
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---
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<h1 align="center">BERT-based Classification Model for Google Local
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<p align="center">
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This repository contains a BERT-based classification model developed using the Hugging Face library, and a dataset gathered by <a href='https://serpapi.com/google-local-api'>SerpApi's Google Local API</a>. The model is designed to classify different texts extracted from Google Local Listings.
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<strong>References:</strong>
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<li>SerpApi's Google Local
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</div>
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</li>
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<li>The model may be susceptible to error in one word entries. This is a minority of the cases, and it could be fixed with assurance scores.For Example:
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<ul style="list-style-type: circle">
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<li><code>"Sushi"</code> → <code>address(0.984), type(0.0493)</code></li>
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<li><code>"Diagorou 4"</code> → <code>address(0.999)</code></li>
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</ul>
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</li>
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<li>The model cannot differentiate between extra parts that are extracted in SerpApi's Google Local API and Google Local Pack API. These parts are not feasible to extract via Classification Models.</li>
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</p>
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<p align="center">
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We appreciate your understanding and support in our commitment to responsible and ethical data practices. If you have any further questions or concerns, please feel free to reach out to us.
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</p>
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- en
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pipeline_tag: text-classification
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widget:
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- title: Rating Example
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text: '4.7'
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- title: Reviews Example
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text: (188)
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- title: Reviews Example 2
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text: '188'
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- title: Reviews Example 3
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text: No Reviews
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- title: Price Example
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text: $
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- title: Type Example
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text: Coffee shop
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- title: Address Example
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text: Frederick, MD
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- title: Address Example 2
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text: 552 W 48th St
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- title: Address Example 3
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text: In Hilton Hotel
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- title: Hours Example
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text: Closed
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- title: Hours Example 2
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text: Opens 7 AM Fri
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- title: Hours Example 3
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text: Permanently closed
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- title: Service Option Example
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text: Dine-in
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- title: Service Option Example 2
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text: Takeout
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- title: Service Option Example 3
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text: Delivery
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- title: Phone Example
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text: (301) 000-0000
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- title: Years In Business Example
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text: 5+ Years in Business
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- title: Button Text Example
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text: Directions
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- title: Description Example
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text: 'Provides: Auto maintenance'
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license: mit
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datasets:
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- serpapi/local-results-en
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---
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<h1 align="center">BERT-based Classification Model for Google Local Listings</h1>
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<p align="center">
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<img src="https://camo.githubusercontent.com/6c920f0b551360ca3257308e0f3547fe538496b9cb332d6a208992030abf6c3d/68747470733a2f2f736572706170692e636f6d2f616e64726f69642d6368726f6d652d353132783531322e706e67" alt="The Logo of SerpApi" width="200" height="200">
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</p>
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<p align="center">
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This repository contains a BERT-based classification model developed using the Hugging Face library, and a dataset gathered by <a href='https://serpapi.com/google-local-api'>SerpApi's Google Local API</a>. The model is designed to classify different texts extracted from Google Local Listings.
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<strong>References:</strong>
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<ul style="text-align: center; list-style-position: inside;">
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<li>SerpApi's Google Local API: <a href ="https://serpapi.com/google-local-api">https://serpapi.com/google-local-api</a></li>
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<li>SerpApi's Google Local Pack API: <a href="https://serpapi.com/local-pack">https://serpapi.com/local-pack</a></li>
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</ul>
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</div>
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</li>
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<li>The model may be susceptible to error in one word entries. This is a minority of the cases, and it could be fixed with assurance scores.For Example:
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<ul style="list-style-type: circle">
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<li><code>"Sushi"</code> → <code>address(0.984), type(0.0493) [Correct Label is type]</code></li>
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<li><code>"Diagorou 4"</code> → <code>address(0.999) [Correct address in same listing]</code></li>
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</ul>
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</li>
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<li>The model cannot differentiate between extra parts that are extracted in SerpApi's Google Local API and Google Local Pack API. These parts are not feasible to extract via Classification Models.</li>
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</p>
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<p align="center">
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We appreciate your understanding and support in our commitment to responsible and ethical data practices. If you have any further questions or concerns, please feel free to reach out to us.
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</p>
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