Instructions to use eunjin/bert_base_cased_syntax_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eunjin/bert_base_cased_syntax_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eunjin/bert_base_cased_syntax_cls")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eunjin/bert_base_cased_syntax_cls") model = AutoModelForSequenceClassification.from_pretrained("eunjin/bert_base_cased_syntax_cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from eunjin/bert_base_cased_syntax_cls: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/eunjin/bert_base_cased_syntax_cls/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://eunjin/bert_base_cased_syntax_cls/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/eunjin/bert_base_cased_syntax_cls/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- b450e2646f22378e2659d106710036438d75fe5f20c37ef19d520dad5269b5e7
- Size of remote file:
- 433 MB
- SHA256:
- f327b42328024b56717b00d53fb5f7660a31284b7253a6c3920a1ca30f7adec5
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