Instructions to use albert/albert-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use albert/albert-base-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="albert/albert-base-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("albert/albert-base-v2") model = AutoModelForMaskedLM.from_pretrained("albert/albert-base-v2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from albert/albert-base-v2: direct link, hf CLI and curl.
- Browser
- Download file 44.9 MB
-
https://huggingface.co/albert/albert-base-v2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://albert/albert-base-v2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/albert/albert-base-v2/resolve/main/flax_model.msgpack
44.9 MB
- Xet hash:
- 601a3fd44a4d6a557fe2fe3ed1fb4f814eb447495fb7011ed69240efef85b8c6
- Size of remote file:
- 44.9 MB
- SHA256:
- 40dc942fcc79e8e88956b39eb097f1a116e7b492c80fc4d5b817c973a4faacc7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.