Instructions to use edugp/data2vec-nlp-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edugp/data2vec-nlp-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="edugp/data2vec-nlp-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("edugp/data2vec-nlp-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Merge branch 'main' of https://huggingface.co/edugp/data2vec-nlp-base
Eduardo Gonzalez Ponferrada07514a1 -
Add RobertaTokenizer from roberta-large checkpoints
Eduardo Gonzalez Ponferrada1b6aa3f -
Create README.md
950b2f0 -
Add converted nlp_base.pt weights from fairseq
Eduardo Gonzalez PonferradaOriginal weights can be found in https://dl.fbaipublicfiles.com/fairseq/data2vec/nlp_base.pt The weights were converted using convert_data2vec_original_pytorch_checkpoint_to_pytorch.py with no classification_headb14207c -
initial commit
eda8dbc