Instructions to use huggingface/CodeBERTa-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingface/CodeBERTa-small-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="huggingface/CodeBERTa-small-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("huggingface/CodeBERTa-small-v1") model = AutoModelForMaskedLM.from_pretrained("huggingface/CodeBERTa-small-v1") - Inference
- 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=8.583e-06; Maximum converted output difference=8.583e-06.
cc @lysandre (HF maintainer for this repo)
Cool
lysandre changed pull request status to merged