Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/rte") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/rte") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93d5d6612fec1fbd4b6c3073f5ba4740c6a21f0163ab660da120a447e52d13e2
- Size of remote file:
- 3.38 kB
- SHA256:
- 8ee4e287180e02df70b66ad7d043e04e00f858ee214abf6cd9f5657a3402f8fb
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