Instructions to use jannesg/takalane_ssw_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jannesg/takalane_ssw_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jannesg/takalane_ssw_roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_ssw_roberta") model = AutoModelForMaskedLM.from_pretrained("jannesg/takalane_ssw_roberta") - Notebooks
- Google Colab
- Kaggle
Takalani Sesame - Tswana πΏπ¦
Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise performance with larger languages around the world.
Intended uses & limitations
How to use
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_ssw_roberta")
model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_ssw_roberta")
Limitations and bias
Updates will be added continously to improve performance.
Training data
Data collected from https://wortschatz.uni-leipzig.de/en
Sentences: 380
Training procedure
No preprocessing. Standard Huggingface hyperparameters.
Author
Jannes Germishuys website
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