Instructions to use Unbabel/XLM-R_L19_H12_FF3072 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Unbabel/XLM-R_L19_H12_FF3072 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Unbabel/XLM-R_L19_H12_FF3072")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Unbabel/XLM-R_L19_H12_FF3072") model = AutoModel.from_pretrained("Unbabel/XLM-R_L19_H12_FF3072") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50e942d2b15e847bede3e92127d349bf425902dcdf9b8512aad5e73abebbd983
- Size of remote file:
- 1.74 GB
- SHA256:
- ec41283958021ae27302632a773f8ded70b8f049373de7cabe903b5d9ff0fd21
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.