Instructions to use qdenisq/BertFormalityClassificiation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qdenisq/BertFormalityClassificiation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="qdenisq/BertFormalityClassificiation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("qdenisq/BertFormalityClassificiation") model = AutoModelForMaskedLM.from_pretrained("qdenisq/BertFormalityClassificiation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from qdenisq/BertFormalityClassificiation: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/qdenisq/BertFormalityClassificiation/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://qdenisq/BertFormalityClassificiation/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/qdenisq/BertFormalityClassificiation/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- e5cc743346a179f73a92985e8730355791f3a4a39369b1c7dbcc2b21389223ab
- Size of remote file:
- 433 MB
- SHA256:
- 7ef96e56741c36a3ec065d8764f7711427631f46d2028c6872f38e084394beb8
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