Instructions to use SebastianS/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SebastianS/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SebastianS/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SebastianS/dummy-model") model = AutoModelForMaskedLM.from_pretrained("SebastianS/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: fr | |
| license: mit | |
| datasets: | |
| - oscar | |
| # dummy | |
| this is only a dummy model originally based on RoBERT model | |
| ## intended uses and limitations | |
| not intended to be used, same limitations as camembert-base model | |
| ## how to use | |
| it cant be used (lol) | |
| ## training data | |
| French subcorpus of the newly available multilingual corpus OSCAR | |
| ## training procedure | |
| evaluated on multiple downstream tasks | |
| ## variable and metrics | |
| not explicitly stated | |
| ## evaluation metrics | |
| maybe OSCAR | |
| ## evaluation results | |
| not explicitly stated | |