Instructions to use almanach/moderncamembert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use almanach/moderncamembert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="almanach/moderncamembert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("almanach/moderncamembert-base") model = AutoModelForMaskedLM.from_pretrained("almanach/moderncamembert-base") - Inference
- Notebooks
- Google Colab
- Kaggle
Is loss metric summed instead of averaged?
#2
by julbo - opened
First, thanks for this amazing model!
I'm under the impression that in training stats the loss metrics are summed instead of averaged: contrary to camembert or camemberta, when I use a validation set of 20%, the reported trainset loss is 4x higher than the testset loss.
This is a bit misleading, see picture, especially wrt other models where the losses are averaged.
Thanks
