Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use edbeeching/test-trainer-to-hub with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edbeeching/test-trainer-to-hub with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="edbeeching/test-trainer-to-hub")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("edbeeching/test-trainer-to-hub") model = AutoModelForSequenceClassification.from_pretrained("edbeeching/test-trainer-to-hub", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6266b6c324caca75582d2d48d4079e0d82f0178eafca85c9053ad83e29ab1a1c
- Size of remote file:
- 2.99 kB
- SHA256:
- b3636ec1b3d983eda20b113624fc303947b3a812f0b0852cecb4e84400716b15
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