Instructions to use Jeevesh8/bert-base-uncased_cola_ft_23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert-base-uncased_cola_ft_23 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert-base-uncased_cola_ft_23")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert-base-uncased_cola_ft_23") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert-base-uncased_cola_ft_23", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Jeevesh8/bert-base-uncased_cola_ft_23: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Jeevesh8/bert-base-uncased_cola_ft_23/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Jeevesh8/bert-base-uncased_cola_ft_23/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Jeevesh8/bert-base-uncased_cola_ft_23/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- c31dc23147f88635a1e2c65f16070fb27f4b8ef5512f37309e9dedf9dab50723
- Size of remote file:
- 438 MB
- SHA256:
- 41238346a8f303e56c21002a87354642df4bf05250f8b84b8630cd1ca7d1aa69
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