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")# pip install -U transformers accelerate # 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") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
c726c59
-
Update README.md
35c14d2 -
Update README.md
133eaf3 -
Upload 2 files
3caab80 -
Delete sentencepiece.bpe.model
6e50af5 -
Update README.md
c2ee4fc -
Update README.md
ae9a99a
-
Update README.md
6ec2baa -
Update README.md
567dd11 -
Update README.md
53e2004 -
Update README.md
b8e9855 -
Upload afrolm.png
ff569b1 -
Delete afrolm.png
6c6759d
-
Update README.md
1a80774 -
Update README.md
2d9a5d7 -
Delete sentencepiece.bpe.vocab
27b518a -
Update tokenizer_config.json
74f6269 -
Update README.md
cae6c4b -
Update README.md
cf3deee -
Update README.md
398b249 -
Upload afrolm.png
a291d55 -
Update README.md
4c5d853 -
Upload README.md
9892ae1
-
initial commit
b8ecdea
-
initial commit
bc231d3