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
- Xet hash:
- b6bbce93d211f214cab0aad064fcaaea18549350e5ff1909086aa66b3a506fc4
- Size of remote file:
- 438 MB
- SHA256:
- 5a6f374dfb2e29ad540e27b0910df121328d18715fb56416dc67ea6a8308adf7
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