Instructions to use Kowsher/model-bangla-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kowsher/model-bangla-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Kowsher/model-bangla-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Kowsher/model-bangla-bert") model = AutoModelForMaskedLM.from_pretrained("Kowsher/model-bangla-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Kowsher/model-bangla-bert: direct link, hf CLI and curl.
- Browser
- Download file 658 MB
-
https://huggingface.co/Kowsher/model-bangla-bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Kowsher/model-bangla-bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Kowsher/model-bangla-bert/resolve/main/pytorch_model.bin
658 MB
- Xet hash:
- 93ee55bb06b40a58f895f5ccf0adb946c4fa1d50a072b2038ae37d7e547a9279
- Size of remote file:
- 658 MB
- SHA256:
- ec7dbad76d0ba9b9d3a6f4a10b1cc8791a3311cca63a008299796a2c6cde6e65
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.