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")# 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") - Notebooks
- Google Colab
- Kaggle
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You may check out the
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You may check out the blog post explaining the model's usecase with an example: <a href="https://serpapi.com/blog/real-world-example-of-ai-powered-parsing/">Real World Example of AI Powered Parsing</a>.
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You may also check out the Open Source Github Repository that contains the source code of a Ruby Gem called <a href="https://github.com/serpapi/google-local-results-ai-parser">`google-local-results-ai-parser`</a>.
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