Instructions to use Connor-tech/bert_cn_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Connor-tech/bert_cn_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Connor-tech/bert_cn_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Connor-tech/bert_cn_finetuning") model = AutoModelForSequenceClassification.from_pretrained("Connor-tech/bert_cn_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Connor-tech/bert_cn_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/Connor-tech/bert_cn_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Connor-tech/bert_cn_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Connor-tech/bert_cn_finetuning/resolve/main/pytorch_model.bin
409 MB
- Xet hash:
- f9718a04607e77094c131ee4eb628321ed4e306c7ede8463446ec89d9761f530
- Size of remote file:
- 409 MB
- SHA256:
- f399c1eb87eddfc644c13189e3706149ecb435bd01ad9905bc056b6b61072379
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