Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use akhousker/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akhousker/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akhousker/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("akhousker/dummy-model") model = AutoModelForSequenceClassification.from_pretrained("akhousker/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 37d03382e3b13d01129b60172115d746f752c04fb816d3d7a447e99d3dab05ff
- Size of remote file:
- 268 MB
- SHA256:
- 2ae89159d429dd2433bb8a627d29e6ec84949a59a4707a79fa2f49cfed03cd98
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