onlysainaa
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Update README.md
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README.md
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@@ -28,32 +28,34 @@ This is the model card of a 🤗 transformers model that has been pushed on the
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- **Finetuned from model [google-t5-small]:** [More Information Needed]
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-
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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tokenizer = AutoTokenizer.from_pretrained("onlysainaa/cyrillic_to_script-t5-model")
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model = AutoModelForSeq2SeqLM.from_pretrained("onlysainaa/cyrillic_to_script-t5-model")
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-
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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-
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input_text = "сайн уу"
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inputs = tokenizer(input_text, return_tensors="pt")
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inputs = {k: v.to(device) for k, v in inputs.items() if k in ['input_ids', 'attention_mask']}
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outputs = model.generate(**inputs)
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translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(f"Translated Text: {translated_text}")
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- **Finetuned from model [google-t5-small]:** [More Information Needed]
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```
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#Load model directly
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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tokenizer = AutoTokenizer.from_pretrained("onlysainaa/cyrillic_to_script-t5-model")
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model = AutoModelForSeq2SeqLM.from_pretrained("onlysainaa/cyrillic_to_script-t5-model")
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#Check if CUDA (GPU) is available
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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#Move the model to the same device (GPU or CPU)
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model.to(device)
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#Prepare text input
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input_text = "сайн уу" #Mongolian greeting
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#Tokenize the input text
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inputs = tokenizer(input_text, return_tensors="pt")
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#Move the input tensors to the same device as the model
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inputs = {k: v.to(device) for k, v in inputs.items() if k in ['input_ids', 'attention_mask']}
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#Generate translation
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outputs = model.generate(**inputs)
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#Decode the output to human-readable text
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translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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#Print the translated text
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print(f"Translated Text: {translated_text}")
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```
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