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:
- 88710321bcb9bd5fe3afde27a4db5eb79310c1ffe4392f4a77c1807a37d3d845
- Size of remote file:
- 438 MB
- SHA256:
- 89afceb3549d4345d87a11741850e4c7ca7c966df50f66a8ac52a2954a00bbf7
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