Instructions to use miladfa7/sanayBERT_model_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miladfa7/sanayBERT_model_V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="miladfa7/sanayBERT_model_V1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("miladfa7/sanayBERT_model_V1") model = AutoModelForMaskedLM.from_pretrained("miladfa7/sanayBERT_model_V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from miladfa7/sanayBERT_model_V1: direct link, hf CLI and curl.
- Browser
- Download file 652 MB
-
https://huggingface.co/miladfa7/sanayBERT_model_V1/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://miladfa7/sanayBERT_model_V1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/miladfa7/sanayBERT_model_V1/resolve/main/flax_model.msgpack
652 MB
- Xet hash:
- 49ca0b5df0a5adf4b5b81bb7faa21c784a97eaf6d77dc091b6a23f6fea2e4988
- Size of remote file:
- 652 MB
- SHA256:
- e798ece6775af532b1ee519c50f96c306ab7fddcd30dfe9153e4719c0a9b842e
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