Instructions to use chihao/bert_cn_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chihao/bert_cn_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chihao/bert_cn_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chihao/bert_cn_finetuning") model = AutoModelForSequenceClassification.from_pretrained("chihao/bert_cn_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from chihao/bert_cn_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/chihao/bert_cn_finetuning/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://chihao/bert_cn_finetuning/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/chihao/bert_cn_finetuning/resolve/main/flax_model.msgpack
409 MB
- Xet hash:
- 9bb3be0dc03472cf7498cab9beee779fb93177ee25a4eab86257bbf24a9f5678
- Size of remote file:
- 409 MB
- SHA256:
- 63211778445a6c24ed4803137001f6461b5f56979860d5225ab9d4e242441f80
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.