Translation
Transformers
PyTorch
TensorFlow
JAX
Rust
Safetensors
mbart
text2text-generation
mbart-50
Instructions to use facebook/mbart-large-50-many-to-many-mmt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mbart-large-50-many-to-many-mmt with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="facebook/mbart-large-50-many-to-many-mmt")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2104dc598d1247ba18c6be20396fe2c8032f76cddf00686d550448b90dc82871
- Size of remote file:
- 2.44 GB
- SHA256:
- 024ddcc796a33d2e4decd4c1bd5fe90ad295aaba9072edb3796a09ef9b755934
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.