Fill-Mask
Transformers
PyTorch
xlm-roberta
afrolm
active learning
language modeling
research papers
natural language processing
self-active learning
Instructions to use bonadossou/afrolm_active_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bonadossou/afrolm_active_learning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bonadossou/afrolm_active_learning")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bonadossou/afrolm_active_learning") model = AutoModelForMaskedLM.from_pretrained("bonadossou/afrolm_active_learning", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from bonadossou/afrolm_active_learning: direct link, hf CLI and curl.
- Browser
- Download file 6.01 MB
-
https://huggingface.co/bonadossou/afrolm_active_learning/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://bonadossou/afrolm_active_learning/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/bonadossou/afrolm_active_learning/resolve/main/sentencepiece.bpe.model
6.01 MB
- Xet hash:
- 5b31d89ed3c043024e0598ab87f34180abedb0a0beb501b374c875ce974c3eba
- Size of remote file:
- 6.01 MB
- SHA256:
- 08b3905a149c62781407670a1308ae9311f70747af186a06a1c19c871f6fc449
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.