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
Download pytorch_model.bin from serpapi/bert-base-local-results: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/serpapi/bert-base-local-results/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://serpapi/bert-base-local-results/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/serpapi/bert-base-local-results/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- b6bbce93d211f214cab0aad064fcaaea18549350e5ff1909086aa66b3a506fc4
- Size of remote file:
- 438 MB
- SHA256:
- 5a6f374dfb2e29ad540e27b0910df121328d18715fb56416dc67ea6a8308adf7
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