Instructions to use olm/olm-roberta-base-oct-2022 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use olm/olm-roberta-base-oct-2022 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="olm/olm-roberta-base-oct-2022")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("olm/olm-roberta-base-oct-2022") model = AutoModelForMaskedLM.from_pretrained("olm/olm-roberta-base-oct-2022") - Notebooks
- Google Colab
- Kaggle
Add TF weights
#1
by joaogante - opened
Model converted by the transformers' pt_to_tf CLI. All converted model outputs and hidden layers were validated against its PyTorch counterpart.
Maximum crossload output difference=4.768e-05; Maximum crossload hidden layer difference=1.431e-05;
Maximum conversion output difference=4.768e-05; Maximum conversion hidden layer difference=1.431e-05;
LGTM thanks!
Tristan changed pull request status to merged