Instructions to use samanjoy2/banglaclickbert_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samanjoy2/banglaclickbert_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="samanjoy2/banglaclickbert_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("samanjoy2/banglaclickbert_base") model = AutoModelForMaskedLM.from_pretrained("samanjoy2/banglaclickbert_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from samanjoy2/banglaclickbert_base: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/samanjoy2/banglaclickbert_base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://samanjoy2/banglaclickbert_base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/samanjoy2/banglaclickbert_base/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 94e48795abd50a21b6fd0c0089a6284fee8d8783a78d93b1b17d11ece0d8ae2a
- Size of remote file:
- 443 MB
- SHA256:
- 4dc7166b4a3f2bd12527040b8015fcb3a0cac1e0af688b245eedd6addfe958a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.