Instructions to use ModelTC/bert-base-uncased-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bert-base-uncased-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bert-base-uncased-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bert-base-uncased-rte") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bert-base-uncased-rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ModelTC/bert-base-uncased-rte: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ModelTC/bert-base-uncased-rte/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ModelTC/bert-base-uncased-rte/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ModelTC/bert-base-uncased-rte/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 27cf7b2145fcc33a86d4850951f79e097108a2c3b924fa2bd98579aff9aa7442
- Size of remote file:
- 438 MB
- SHA256:
- bc0293bd2736130884861f5891b9822e1e2109d2a8c4d6bf8db648b04b080ea5
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