Instructions to use fairnlp/albert-cda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fairnlp/albert-cda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fairnlp/albert-cda")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fairnlp/albert-cda") model = AutoModel.from_pretrained("fairnlp/albert-cda", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fairnlp/albert-cda: direct link, hf CLI and curl.
- Browser
- Download file 70.7 MB
-
https://huggingface.co/fairnlp/albert-cda/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fairnlp/albert-cda/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fairnlp/albert-cda/resolve/main/pytorch_model.bin
70.7 MB
- Xet hash:
- ad0c4ba48e552d480b7a51fcf13b36c550b9ca085b0bc475e62704b79ed9b48d
- Size of remote file:
- 70.7 MB
- SHA256:
- 4be1de2ef8ac53c78aafdd9b2986dfea38a8c321d1a75f0288eed9426d2b517d
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