Instructions to use tiya1012/crossde_BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/crossde_BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/crossde_BERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/crossde_BERT") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/crossde_BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/crossde_BERT: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/tiya1012/crossde_BERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/crossde_BERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/crossde_BERT/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- e0a1a1890c2fb6fa9ca817291c43d170f808da7bdf235acf1b96e50552150265
- Size of remote file:
- 438 MB
- SHA256:
- c88e865cefaa6ea5cf9536d72cedf71ad8e707a6ea7afbbdd5c8f3c17bfe5d54
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