Instructions to use NTCAL/SavedAfterTrainingTest39 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/SavedAfterTrainingTest39 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/SavedAfterTrainingTest39")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/SavedAfterTrainingTest39") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/SavedAfterTrainingTest39") - 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 sha256:954f21f81be2b74b4000fc51e085b4ec2838786ef5fb02546ff5846dcb19378e
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size 498118752
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