Instructions to use gngpostalsrvc/BERiTmodel2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gngpostalsrvc/BERiTmodel2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gngpostalsrvc/BERiTmodel2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gngpostalsrvc/BERiTmodel2") model = AutoModelForMaskedLM.from_pretrained("gngpostalsrvc/BERiTmodel2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gngpostalsrvc/BERiTmodel2: direct link, hf CLI and curl.
- Browser
- Download file 490 MB
-
https://huggingface.co/gngpostalsrvc/BERiTmodel2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gngpostalsrvc/BERiTmodel2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gngpostalsrvc/BERiTmodel2/resolve/main/pytorch_model.bin
490 MB
- Xet hash:
- 1809123e0ef70b994b5eeb19b340c1914ae0374bea306be9ced7b0e67cca63cb
- Size of remote file:
- 490 MB
- SHA256:
- e7ac9e7f0da5f40f6aa7d9137f89070538d575c8444be5af78667e6c41c36d22
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