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README.md
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library_name: transformers
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tags: []
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widget:
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- text: 'Please correct the following sentence: '
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example_title: Spelling Correction
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---
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tokenizer = T5Tokenizer.from_pretrained("thaboe01/t5-spelling-corrector-ndebele")
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model = T5ForConditionalGeneration.from_pretrained("thaboe01/t5-spelling-corrector-ndebele")
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input_text = "Please correct the following sentence: "
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids
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outputs = model.generate(input_ids)
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tokenizer = T5Tokenizer.from_pretrained("thaboe01/t5-spelling-corrector-ndebele")
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model = T5ForConditionalGeneration.from_pretrained("thaboe01/t5-spelling-corrector-ndebele", device_map="auto")
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input_text = "Please correct the following sentence: "
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
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outputs = model.generate(input_ids)
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tokenizer = T5Tokenizer.from_pretrained("thaboe01/t5-spelling-corrector-ndebele")
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model = T5ForConditionalGeneration.from_pretrained("thaboe01/t5-spelling-corrector-ndebele", device_map="auto", torch_dtype=torch.float16)
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input_text = "Please correct the following sentence: "
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
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outputs = model.generate(input_ids)
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tokenizer = T5Tokenizer.from_pretrained("thaboe01/t5-spelling-corrector-ndebele")
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model = T5ForConditionalGeneration.from_pretrained("thaboe01/t5-spelling-corrector-ndebele", device_map="auto", load_in_8bit=True)
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input_text = "Please correct the following sentence: "
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
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outputs = model.generate(input_ids)
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library_name: transformers
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tags: []
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widget:
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- text: 'Please correct the following sentence: ukuti yiles sivnmelwano'
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example_title: Spelling Correction
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---
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tokenizer = T5Tokenizer.from_pretrained("thaboe01/t5-spelling-corrector-ndebele")
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model = T5ForConditionalGeneration.from_pretrained("thaboe01/t5-spelling-corrector-ndebele")
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input_text = "Please correct the following sentence: ukuti yiles sivnmelwano"
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids
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outputs = model.generate(input_ids)
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tokenizer = T5Tokenizer.from_pretrained("thaboe01/t5-spelling-corrector-ndebele")
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model = T5ForConditionalGeneration.from_pretrained("thaboe01/t5-spelling-corrector-ndebele", device_map="auto")
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input_text = "Please correct the following sentence: ukuti yiles sivnmelwano"
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
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outputs = model.generate(input_ids)
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tokenizer = T5Tokenizer.from_pretrained("thaboe01/t5-spelling-corrector-ndebele")
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model = T5ForConditionalGeneration.from_pretrained("thaboe01/t5-spelling-corrector-ndebele", device_map="auto", torch_dtype=torch.float16)
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input_text = "Please correct the following sentence: ukuti yiles sivnmelwano"
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
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outputs = model.generate(input_ids)
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tokenizer = T5Tokenizer.from_pretrained("thaboe01/t5-spelling-corrector-ndebele")
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model = T5ForConditionalGeneration.from_pretrained("thaboe01/t5-spelling-corrector-ndebele", device_map="auto", load_in_8bit=True)
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input_text = "Please correct the following sentence: ukuti yiles sivnmelwano"
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
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outputs = model.generate(input_ids)
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