84f10092e9
- Removed M2M100 Grammar Correction model completely to reduce bloat/complexity. - Implemented 'Style Prompting' in Settings -> AI Engine to handle punctuation natively via Whisper. - Added Style Presets: Standard (Default), Casual, and Custom. - Optimized Build: Bootstrapper no longer requires transformers/sentencepiece. - Fixed 'torch' NameError in Low VRAM mode. - Fixed Bootstrapper missing dependency detection. - Updated UI to reflect removed features. - Included compiled v1.0.2 Executable in dist/.
41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
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import sys
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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def test_mt0():
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model_name = "bigscience/mt0-base"
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print(f"Loading {model_name}...")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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# Test cases: (Language, Prompt, Input)
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# MT0 is instruction tuned, so we should prompt it in the target language or English.
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# Cross-lingual prompting (English prompt -> Target tasks) is usually supported.
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test_cases = [
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("English", "Correct grammar:", "he go to school yesterday"),
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("Polish", "Popraw gramatykę:", "to jest testowe zdanie bez kropki"),
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("Finnish", "Korjaa kielioppi:", "tämä on testilause ilman pistettä"),
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("Russian", "Исправь грамматику:", "это тестовое предложение без точки"),
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("Japanese", "文法を直してください:", "これは点のないテスト文です"),
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("Spanish", "Corrige la gramática:", "esta es una oración de prueba sin punto"),
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]
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print("\nStarting MT0 Tests:\n")
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for lang, prompt_text, input_text in test_cases:
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full_input = f"{prompt_text} {input_text}"
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inputs = tokenizer(full_input, return_tensors="pt")
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outputs = model.generate(inputs.input_ids, max_length=128)
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corrected = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(f"[{lang}]")
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print(f"Input: {full_input}")
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print(f"Output: {corrected}")
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print("-" * 20)
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if __name__ == "__main__":
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test_mt0()
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