Translation
Transformers
Safetensors
Tibetan
English
t5
text2text-generation
text-generation-inference
Instructions to use billingsmoore/prototype-tibetan-to-english-translation-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use billingsmoore/prototype-tibetan-to-english-translation-v0 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="billingsmoore/prototype-tibetan-to-english-translation-v0")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("billingsmoore/prototype-tibetan-to-english-translation-v0") model = AutoModelForSeq2SeqLM.from_pretrained("billingsmoore/prototype-tibetan-to-english-translation-v0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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data = load_dataset(<path_to_your_dataset>)
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checkpoint = "billingsmoore/
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)
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data = load_dataset(<path_to_your_dataset>)
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checkpoint = "billingsmoore/phonetic-tibetan-to-english-translation"
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)
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