Instructions to use CadenzaBaron/M2M100-418M-for-GameTranslation-Finetuned-Zh-En with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CadenzaBaron/M2M100-418M-for-GameTranslation-Finetuned-Zh-En 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="CadenzaBaron/M2M100-418M-for-GameTranslation-Finetuned-Zh-En")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("CadenzaBaron/M2M100-418M-for-GameTranslation-Finetuned-Zh-En") model = AutoModelForSeq2SeqLM.from_pretrained("CadenzaBaron/M2M100-418M-for-GameTranslation-Finetuned-Zh-En") - Notebooks
- Google Colab
- Kaggle
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README.md
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This is a finetuned version of Facebook/M2M100.
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It has been trained on a parallel corpus on several Chinese video games translations. All of them are from human/fan translations.
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It's not perfect but it's the best I could do.
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It should be sitting somewhere between Google Translate and DeepL, I guess.
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So... Before you go any further, lower your expectations.
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This is a finetuned version of Facebook/M2M100.
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It's a project born from the activity of [Amateur Modding Avenue](discord.gg/agFA6xa6un), a Discord based modding community.
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Special thanks to the Path of Wuxia modding team for kindly sharing their translations to help build the dataset.
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It has been trained on a parallel corpus on several Chinese video games translations. All of them are from human/fan translations.
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It's not perfect but it's the best I could do.
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It should be sitting somewhere between Google Translate and DeepL, I guess.
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So... Before you go any further, lower your expectations.
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