Instructions to use tk3879110/bert_cn_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tk3879110/bert_cn_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tk3879110/bert_cn_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tk3879110/bert_cn_finetuning") model = AutoModelForSequenceClassification.from_pretrained("tk3879110/bert_cn_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tk3879110/bert_cn_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
-
https://huggingface.co/tk3879110/bert_cn_finetuning/resolve/main/training_args.bin
- Command line
-
hf download hf://tk3879110/bert_cn_finetuning/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tk3879110/bert_cn_finetuning/resolve/main/training_args.bin
1.24 kB
- Xet hash:
- 46856f4869acab44999dee98b9854e33848b1d45e72b07c82cc2cdb08c3a6be4
- Size of remote file:
- 1.24 kB
- SHA256:
- 438022aeb8ea50c2cba7904c1ab7d9c205700dc9364efc333230b87e3525472d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.