Instructions to use abdouaziiz/bert-base-wolof with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abdouaziiz/bert-base-wolof with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="abdouaziiz/bert-base-wolof")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("abdouaziiz/bert-base-wolof") model = AutoModelForMaskedLM.from_pretrained("abdouaziiz/bert-base-wolof", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from abdouaziiz/bert-base-wolof: direct link, hf CLI and curl.
- Browser
- Download file 228 MB
-
https://huggingface.co/abdouaziiz/bert-base-wolof/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://abdouaziiz/bert-base-wolof/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/abdouaziiz/bert-base-wolof/resolve/main/pytorch_model.bin
228 MB
- Xet hash:
- 56f4941eac262e2509b323b5b02d7be5efe4145a9f244bec90e9fcb6dd5b04b0
- Size of remote file:
- 228 MB
- SHA256:
- d275b9c25c9574db1b4ebe5900ae3ee6f9552e9aff52f04b81f29bc4cf1e103b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.