Instructions to use calvegh/experimental_topic_classification_GPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use calvegh/experimental_topic_classification_GPU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="calvegh/experimental_topic_classification_GPU")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("calvegh/experimental_topic_classification_GPU") model = AutoModelForSequenceClassification.from_pretrained("calvegh/experimental_topic_classification_GPU") - 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 sha256:1bc4c7de499b2d22dcd19ad4904e2383a65bd6a8d0e9a008315ef63b8fb5f117
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size 1884064652
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