Translation
Transformers
PyTorch
JAX
TensorBoard
Dutch
English
t5
text2text-generation
seq2seq
text-generation-inference
Instructions to use yhavinga/t5-base-36L-ccmatrix-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yhavinga/t5-base-36L-ccmatrix-multi 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="yhavinga/t5-base-36L-ccmatrix-multi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yhavinga/t5-base-36L-ccmatrix-multi") model = AutoModelForSeq2SeqLM.from_pretrained("yhavinga/t5-base-36L-ccmatrix-multi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Autoupdate README.md
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Autoupdate README.md
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Save weights at end of training
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Saving weights at step 350k, Loss: 0.5368054509162903, Bleu: 46.2924345865966
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Save weights
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Saving weights and log
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initial commit
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