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/.
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
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import sys
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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
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def test_m2m():
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model_name = "facebook/m2m100_418M"
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print(f"Loading {model_name}...")
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tokenizer = M2M100Tokenizer.from_pretrained(model_name)
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model = M2M100ForConditionalGeneration.from_pretrained(model_name)
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# Test cases: (Language Code, Input)
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test_cases = [
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("en", "he go to school yesterday"),
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("pl", "on iść do szkoła wczoraj"), # Intentional broken grammar in Polish
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]
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print("\nStarting M2M Tests (Self-Translation):\n")
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for lang, input_text in test_cases:
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tokenizer.src_lang = lang
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encoded = tokenizer(input_text, return_tensors="pt")
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# Translate to SAME language
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generated_tokens = model.generate(
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**encoded,
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forced_bos_token_id=tokenizer.get_lang_id(lang)
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)
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corrected = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
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print(f"[{lang}]")
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print(f"Input: {input_text}")
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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_m2m()
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