Instructions to use joaogante/test_text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joaogante/test_text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="joaogante/test_text")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("joaogante/test_text") model = AutoModelForMaskedLM.from_pretrained("joaogante/test_text") - Notebooks
- Google Colab
- Kaggle
Add TF weights
#11
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=3.767e-05; Maximum crossload hidden layer difference=1.049e-05;
Maximum conversion output difference=3.767e-05; Maximum conversion hidden layer difference=1.049e-05;