Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ginnigarg/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ginnigarg/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ginnigarg/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ginnigarg/dummy-model") model = AutoModelForSequenceClassification.from_pretrained("ginnigarg/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b523d1a724533bf22b15e8ec27bbb5ada10d6307409c92f5de5a01c0688dd790
- Size of remote file:
- 438 MB
- SHA256:
- 1273c0a78409eb1c5b8663c863ed51ad547b5d537a2e4a43b7030e76eb0f5d3a
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