Instructions to use m3hrdadfi/albert-fa-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m3hrdadfi/albert-fa-base-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="m3hrdadfi/albert-fa-base-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("m3hrdadfi/albert-fa-base-v2") model = AutoModelForMaskedLM.from_pretrained("m3hrdadfi/albert-fa-base-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from m3hrdadfi/albert-fa-base-v2: direct link, hf CLI and curl.
- Browser
- Download file 73.1 MB
-
https://huggingface.co/m3hrdadfi/albert-fa-base-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://m3hrdadfi/albert-fa-base-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/m3hrdadfi/albert-fa-base-v2/resolve/main/pytorch_model.bin
73.1 MB
- Xet hash:
- 1d79c96c4aceb56e669062130047a414ddabcbe234a51e7bee0e78cab0e07930
- Size of remote file:
- 73.1 MB
- SHA256:
- ef549a9158ad4c09b8211f7f3e8765395b94e30344d0cb542dfbf8c49bf74d93
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