Instructions to use tiya1012/swmh4_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/swmh4_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/swmh4_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/swmh4_bert") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/swmh4_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/swmh4_bert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/tiya1012/swmh4_bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/swmh4_bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/swmh4_bert/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- ed4f0a83def9ffd5b59b23590bd71b6c6de6b26ec8f687c0f31d1744dbabdcbd
- Size of remote file:
- 438 MB
- SHA256:
- 2b7672abf3f3a2e12d4f7536fd7f5be67d060bd6b05356195577e45996f2a078
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