Translation
Transformers
TensorBoard
Safetensors
marian
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use sauc-abadal-lloret/opus-mt-ca-en-ft-kde4-mt-ca-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sauc-abadal-lloret/opus-mt-ca-en-ft-kde4-mt-ca-en 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="sauc-abadal-lloret/opus-mt-ca-en-ft-kde4-mt-ca-en")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("sauc-abadal-lloret/opus-mt-ca-en-ft-kde4-mt-ca-en") model = AutoModelForMultimodalLM.from_pretrained("sauc-abadal-lloret/opus-mt-ca-en-ft-kde4-mt-ca-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ea781310501f686cbfdf5e527733a359864014e32dc3db489404d44f88f3716
- Size of remote file:
- 290 MB
- SHA256:
- d315c75d7f7c4d960a56a669f565332b33cad932364a13904162f07a53abd99e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.