Instructions to use Mahmoud8/albert-large-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud8/albert-large-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud8/albert-large-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud8/albert-large-v2") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud8/albert-large-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mahmoud8/albert-large-v2: direct link, hf CLI and curl.
- Browser
- Download file 70.8 MB
-
https://huggingface.co/Mahmoud8/albert-large-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mahmoud8/albert-large-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mahmoud8/albert-large-v2/resolve/main/pytorch_model.bin
70.8 MB
- Xet hash:
- ae7c19fd137621ea9034c27458e365c94f4e6dec400e14ae21583229524468a1
- Size of remote file:
- 70.8 MB
- SHA256:
- f4adcb3dc6e42f2fa670a00014425f2ff83028a7e7f51996c1bac30ca186cd1f
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