Instructions to use JunxiongWang/BiGS_512_MNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/BiGS_512_MNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JunxiongWang/BiGS_512_MNLI")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("JunxiongWang/BiGS_512_MNLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from JunxiongWang/BiGS_512_MNLI: direct link, hf CLI and curl.
- Browser
- Download file 1.39 GB
-
https://huggingface.co/JunxiongWang/BiGS_512_MNLI/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://JunxiongWang/BiGS_512_MNLI/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/JunxiongWang/BiGS_512_MNLI/resolve/main/flax_model.msgpack
1.39 GB
- Xet hash:
- 38d2d3f5babc7a82913c34f4ec77b815c1cf210836c92e479d4a2445eaf783fb
- Size of remote file:
- 1.39 GB
- SHA256:
- c604c2ae56f9b2d02d1850edc4ce345c50928ef5b713a8c0923cf8c347e43f18
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