Instructions to use Bainbridge/bert-incl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bainbridge/bert-incl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Bainbridge/bert-incl")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Bainbridge/bert-incl") model = AutoModelForSequenceClassification.from_pretrained("Bainbridge/bert-incl") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
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 sha256:3dc5b32eaa7e74741cae73a61ab35fb82ef8e5339f6c191e043eacfa19f114a0
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size 439744592
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