Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use LightFury9/out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LightFury9/out with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LightFury9/out")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LightFury9/out") model = AutoModelForSequenceClassification.from_pretrained("LightFury9/out") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ed74375f1359a396f87ece1f8c1fdda519593fddb0a39bb733ecbb28be16b20
- Size of remote file:
- 5.24 kB
- SHA256:
- daf9f6e48e7c109515bc84e793c4bcfedea17d802744b87d58086b4ca716c927
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