Instructions to use rajad/albert_type2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajad/albert_type2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rajad/albert_type2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rajad/albert_type2") model = AutoModelForSequenceClassification.from_pretrained("rajad/albert_type2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from rajad/albert_type2: direct link, hf CLI and curl.
- Browser
- Download file 46.8 MB
-
https://huggingface.co/rajad/albert_type2/resolve/main/model.safetensors
- Command line
-
hf download hf://rajad/albert_type2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/rajad/albert_type2/resolve/main/model.safetensors
46.8 MB
- Xet hash:
- 913d86a45e5c51cd2945ff94be8a78108185f86e28f640b928aa9cf1f74b4d06
- Size of remote file:
- 46.8 MB
- SHA256:
- e5a03e392c373ba68946135dabc0451d4c7dc20b40ebf6a3fac3375edeee7cb7
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