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:
- 258fbc161f3ca4ce43f9285c6917d8acfa6991df4ade2cdd664cf01a3f3d77e2
- Size of remote file:
- 4.73 kB
- SHA256:
- 8373ed18c9214156281ab3d0a7bb5265233a2b8677ab73d883224dfe5d428b63
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