Text Classification
Transformers
PyTorch
Safetensors
English
bert
rte
glue
torchdistill
text-embeddings-inference
Instructions to use yoshitomo-matsubara/bert-base-uncased-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yoshitomo-matsubara/bert-base-uncased-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yoshitomo-matsubara/bert-base-uncased-rte")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yoshitomo-matsubara/bert-base-uncased-rte") model = AutoModelForSequenceClassification.from_pretrained("yoshitomo-matsubara/bert-base-uncased-rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model (#1)
Browse files- Adding `safetensors` variant of this model (755338d7904a9231013a2cc4e2f1c88ae90e4d7d)
Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>
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