Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use dd123/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dd123/test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dd123/test_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dd123/test_model") model = AutoModelForSequenceClassification.from_pretrained("dd123/test_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9095cb9684f921bd1d2dac84317dcc09476164f43a46acaa3ec692aad3b33b68
- Size of remote file:
- 438 MB
- SHA256:
- 56b21ef0c3a9003df82734cee4cc9a214f22bb3770e083a719c45983a759611e
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