Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Hansaht/Text_classification_model_2_Tenserflow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hansaht/Text_classification_model_2_Tenserflow with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hansaht/Text_classification_model_2_Tenserflow")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hansaht/Text_classification_model_2_Tenserflow") model = AutoModelForSequenceClassification.from_pretrained("Hansaht/Text_classification_model_2_Tenserflow", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af5de772c41068db4a3f1db92c5db645a4a59108bd738d4e33575d34f2614c86
- Size of remote file:
- 268 MB
- SHA256:
- d1f5a07279ee98076aa8204dfc92b36a1f5c1116cb6c905c5ea7210ab3b0c016
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