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 training_args.bin from gngpostalsrvc/BERiTmodel2: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/gngpostalsrvc/BERiTmodel2/resolve/main/training_args.bin
- Command line
-
hf download hf://gngpostalsrvc/BERiTmodel2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gngpostalsrvc/BERiTmodel2/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- 8383f778a29da9ae2566474c11fc7f6a72df46da1e486f60fc660d199f1a9564
- Size of remote file:
- 2.93 kB
- SHA256:
- 1fadfee747b13f17eb98b5cec8187a7831281c7abd8da2ed90c5a72df86444c1
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