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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/rte") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from henryscheible/rte: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/rte/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/rte/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/rte/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- eac10dd0599ea764c67589b5a9022adf57c0e92d743f7a16e6a9af2810aa7ee1
- Size of remote file:
- 438 MB
- SHA256:
- 518b72481cf02ab8e6c68db2ea2ea1b700e84b6f1686ea4462e88dc73b315029
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