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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9bd6492ec856865d3014562a6aef10779f683e1c10b77f76b2c7b39f46cecdbf
- Size of remote file:
- 1.06 GB
- SHA256:
- ed05b34b0c1176115394ff56481c4bdb3dc548c6503e7b85bd167fc1ca53dc44
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