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")# 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
Download pytorch_model.bin from yoshitomo-matsubara/bert-base-uncased-rte: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/yoshitomo-matsubara/bert-base-uncased-rte/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://yoshitomo-matsubara/bert-base-uncased-rte/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/yoshitomo-matsubara/bert-base-uncased-rte/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 5a994b7ae6f36b37cd5849c158d2b3d8a823cdc2461fb37b2987fd62c4e26b21
- Size of remote file:
- 438 MB
- SHA256:
- b6f7de7e563064d7239c48e250e0cdfe4ecd69d808558e620f56e2806f84e383
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