Instructions to use liuchenyang33/bert_cn_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liuchenyang33/bert_cn_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="liuchenyang33/bert_cn_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("liuchenyang33/bert_cn_finetuning") model = AutoModelForSequenceClassification.from_pretrained("liuchenyang33/bert_cn_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from liuchenyang33/bert_cn_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/liuchenyang33/bert_cn_finetuning/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://liuchenyang33/bert_cn_finetuning/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/liuchenyang33/bert_cn_finetuning/resolve/main/flax_model.msgpack
409 MB
- Xet hash:
- 64d9bbabb5a8699323820e16db858b27d7bcd14f7509c4d0c9382cc5775b8c46
- Size of remote file:
- 409 MB
- SHA256:
- 2d91f76549899fa0c25b959844ed8b8eebc98575dcfaa6e355a1eb7c48317f2d
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