Instructions to use jh02/bert_nsmc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jh02/bert_nsmc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jh02/bert_nsmc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jh02/bert_nsmc") model = AutoModelForSequenceClassification.from_pretrained("jh02/bert_nsmc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jh02/bert_nsmc: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/jh02/bert_nsmc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jh02/bert_nsmc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jh02/bert_nsmc/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 08cd5abac32ac9bd6561f63d8aede9bbae1ef51d898f84cb046dce19c627797d
- Size of remote file:
- 443 MB
- SHA256:
- f79ad116a9c15a5b93caacf5177bef15851d8aa430a22bab552c6d25f47aad20
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