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
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0bfa73285875ee42bfe040d3e49bd0380613b60c8ec0bf53d86419838c7bdbd1
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size 437990516
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