Transformers
PyTorch
TensorFlow
JAX
English
bert
pretraining
singapore
sg
singlish
malaysia
ms
manglish
bert-large-uncased
Instructions to use zanelim/singbert-large-sg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zanelim/singbert-large-sg with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("zanelim/singbert-large-sg") model = AutoModelForPreTraining.from_pretrained("zanelim/singbert-large-sg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from zanelim/singbert-large-sg: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/zanelim/singbert-large-sg/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://zanelim/singbert-large-sg/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/zanelim/singbert-large-sg/resolve/main/flax_model.msgpack
1.34 GB
- Xet hash:
- 9f8666ac20d3eed9386e87471d96c479efe86e15873bab798a1e8116146393a3
- Size of remote file:
- 1.34 GB
- SHA256:
- 10c9fad642fbab6b0568928a315e86c9473b8c537c8c0e22489fefc0d24ff068
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.