Instructions to use amine/bert-base-5lang-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amine/bert-base-5lang-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="amine/bert-base-5lang-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("amine/bert-base-5lang-cased") model = AutoModelForMaskedLM.from_pretrained("amine/bert-base-5lang-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from amine/bert-base-5lang-cased: direct link, hf CLI and curl.
- Browser
- Download file 495 MB
-
https://huggingface.co/amine/bert-base-5lang-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://amine/bert-base-5lang-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/amine/bert-base-5lang-cased/resolve/main/flax_model.msgpack
495 MB
- Xet hash:
- 0f5ba9f21d5ffedbdf608c284c2ab64de42de067ec9060de0815911d3d802419
- Size of remote file:
- 495 MB
- SHA256:
- 1b1b19362eb625bdc2407ccbeab7abc60eab7055968c74a5dab936ed2ef3d9b8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.