Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use rcade/testing_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rcade/testing_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rcade/testing_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rcade/testing_model") model = AutoModelForSequenceClassification.from_pretrained("rcade/testing_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4be959e5abdbfe231139c42e2a4120fd4c85740f4198824ef044a665b65ad8a4
- Size of remote file:
- 433 MB
- SHA256:
- f024dc3d3a2c00a479af46afb1d91b5a617b3e2ea27cb4429eddded6ec9699b9
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