Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Kamer/UniqueProcessedText with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kamer/UniqueProcessedText with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kamer/UniqueProcessedText")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kamer/UniqueProcessedText") model = AutoModelForSequenceClassification.from_pretrained("Kamer/UniqueProcessedText") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db98a2a76dc47d01f9b445598d916ee4ce7f5a20528af9e381d44ef5ea0c84a5
- Size of remote file:
- 268 MB
- SHA256:
- 3f69fbbb7b6d76fbac14019a8578ed0bb86e92e24488ea985883766d81db3d2e
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