Instructions to use JunxiongWang/BiGS_128_MNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/BiGS_128_MNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JunxiongWang/BiGS_128_MNLI")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("JunxiongWang/BiGS_128_MNLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from JunxiongWang/BiGS_128_MNLI: direct link, hf CLI and curl.
- Browser
- Download file 1.39 GB
-
https://huggingface.co/JunxiongWang/BiGS_128_MNLI/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://JunxiongWang/BiGS_128_MNLI/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/JunxiongWang/BiGS_128_MNLI/resolve/main/flax_model.msgpack
1.39 GB
- Xet hash:
- 0ac7a9f2338797b6caee755cfb18761615d0238fb31490ececfc4cd2ca42f5ad
- Size of remote file:
- 1.39 GB
- SHA256:
- a348bf369807ae3cd74016b19f346924ec52200e5b6fdf70ff8e2b6a33d627e6
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