Instructions to use castorini/afriberta_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/afriberta_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="castorini/afriberta_small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("castorini/afriberta_small") model = AutoModelForMaskedLM.from_pretrained("castorini/afriberta_small", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from castorini/afriberta_small: direct link, hf CLI and curl.
- Browser
- Download file 333 MB
-
https://huggingface.co/castorini/afriberta_small/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://castorini/afriberta_small/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/castorini/afriberta_small/resolve/main/pytorch_model.bin
333 MB
- Xet hash:
- 3954332399115320e6cfea66908d51bc324583333bae1f5a67ea585b78564c33
- Size of remote file:
- 333 MB
- SHA256:
- 6c36356f7e34b57706c46e4fe5e8e013e91441990a7ec2eb046ff315bca05069
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.