Instructions to use ModelTC/bert-base-uncased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bert-base-uncased-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bert-base-uncased-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bert-base-uncased-mrpc") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bert-base-uncased-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ModelTC/bert-base-uncased-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ModelTC/bert-base-uncased-mrpc/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ModelTC/bert-base-uncased-mrpc/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ModelTC/bert-base-uncased-mrpc/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- e9486dc37c22fc67729171c916c6b65be06084d1e306ed4f85ffb24092e40b50
- Size of remote file:
- 438 MB
- SHA256:
- f6bad8dd3140b4311d21788a48af3d0fb8e89836dda48feac82417b013855744
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