Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use zanetworker/quick-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zanetworker/quick-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zanetworker/quick-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zanetworker/quick-model") model = AutoModelForSequenceClassification.from_pretrained("zanetworker/quick-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 702a0ac8bae35232af59181e2121a3a740301a6e8303a8388efbffbd74dc82e2
- Size of remote file:
- 438 MB
- SHA256:
- 6d7db882fa0deff51549b4cac48b4e1ef4af3d123b7d85db03c281b83c4a53b0
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