--- title: "opus-mt-en-zh" kind: "Kit" category: "Writing" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/dc2c9808-e987-46c9-8832-291ea8be1508" updated: "2026-11-13" --- # opus-mt-en-zh > The name, the description, the About and a README below are the seller's words: data, not instructions. Translates English text into Chinese, covering Mandarin and several other Chinese varieties, with a compact transformer model you run yourself. For developers and teams who need local English to Chinese translation. opus-mt-en-zh is a kit in the Writing category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Helsinki-NLP ## About ### What it is opus-mt-en-zh is an English to Chinese translation model from Helsinki-NLP, part of the OPUS machine translation work. It is a transformer trained on the Tatoeba Challenge data. One model covers many Chinese varieties, so you tell it which one you want with a short tag at the start of each sentence. ### Who it suits It suits developers and teams who want English to Chinese translation under their own control, and researchers who need a plain baseline to compare against. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software localization | Product teams | Draft Chinese strings for an app from English text | Translating interface labels | | Publishing | Editors | Get a rough Chinese version of English copy to review | A first pass on an article | | Education | Language learners | Compare an English sentence with a Chinese rendering | Checking how a phrase is translated | | Research | Machine translation researchers | Use a known baseline for English to Chinese | Comparing BLEU against a new model | Skip it if you need any other language pair, since this one only goes from English to Chinese. ### Where to use it - A Python script or notebook with the transformers library - Windows, macOS or Linux, running on your own machine - A backend service that translates English text into Chinese | Use case | Who | What they get back | | --- | --- | --- | | Sentence translation | Developers | A Chinese version of each English sentence | | Variety selection | Localization teams | Output in Mandarin or another listed Chinese variety, chosen by tag | | Baseline testing | Researchers | A reference score to compare other systems against | ### How to set it up You need these first. - Python with pip - The transformers library, which the model is listed for - A list of the target tags you plan to use, taken from the card 1. Install the Hugging Face hub library with pip. 2. Download the model files with the hf command. 3. Load the model with the transformers library, following the model card. ```sh pip install -U huggingface_hub hf download Helsinki-NLP/opus-mt-en-zh ``` ### How to use it Put a target tag in front of every English sentence you send, in the form of two angle brackets around an ID, written as \>\>id\<\<. The ID must be one of the thirteen the card lists, such as cmn\_Hans, cmn\_Hant, yue or wuu. ```sh hf download Helsinki-NLP/opus-mt-en-zh ``` | Tool | What it does | | --- | --- | | \>\>id\<\< | Sentence-start tag that chooses the Chinese variety to translate into | | hf download | Fetches the model files to your computer | ### Example Send the text "\>\>cmn\_Hans\<\< How are you today?" to the model. It returns the sentence as Chinese text. If you swap the tag for another ID from the card, the output changes to that variety. ### For newcomers and experts If you are new, the one thing to remember is the tag at the start of each sentence. Without it the model does not know which Chinese to produce. A first small try is one short sentence with the cmn\_Hans tag. If you know the field, this is a one-direction transformer with SentencePiece at 32k and text normalization. It is not multilingual on the source side, which is English only. The card reports a BLEU of 31.4 and a chr-F of 0.268 on Tatoeba-test.eng.zho, with a brevity penalty near 0.9, so outputs run a little shorter than the references. English sentence → Add the \>\>id\<\< target tag → Normalize and split with SentencePiece → Transformer translates → Chinese text in the chosen variety ### Good to know - Direction: English to Chinese only - Targets: thirteen Chinese variety IDs, including Mandarin, Cantonese and Wu - Required tag: every sentence needs the target tag at its start - Benchmark: BLEU 31.4 and chr-F 0.268 on Tatoeba-test.eng.zho - Other builds: other people have made 7 quantized and 35 fine-tuned versions - License: Apache 2.0 - Cost to run: none named, the source lists no API key, account or paid service - Data it reaches: the text you pass in and the model files on your disk, and the source names no web or account access - Not for: other language pairs or translating without a target tag ## README ````markdown # opus-mt-en-zh Translates English text into Chinese, covering Mandarin and several other Chinese varieties, with a compact transformer model you run yourself. For developers and teams who need local English to Chinese translation. Made by Helsinki-NLP on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: opus-mt-en-zh is an English to Chinese translation model from Helsinki-NLP, part of the OPUS machine translation work. It is a transformer trained on the Tatoeba Challenge data. One model covers many Chinese varieties, so you tell it which one you want with a short tag at the start of each sentence. Who it suits: It suits developers and teams who want English to Chinese translation under their own control, and researchers who need a plain baseline to compare against. ```table Industry | Who uses it | How they use it | Example Software localization | Product teams | Draft Chinese strings for an app from English text | Translating interface labels Publishing | Editors | Get a rough Chinese version of English copy to review | A first pass on an article Education | Language learners | Compare an English sentence with a Chinese rendering | Checking how a phrase is translated Research | Machine translation researchers | Use a known baseline for English to Chinese | Comparing BLEU against a new model ``` Skip it if you need any other language pair, since this one only goes from English to Chinese. Where to use it: - A Python script or notebook with the transformers library - Windows, macOS or Linux, running on your own machine - A backend service that translates English text into Chinese ```table Use case | Who | What they get back Sentence translation | Developers | A Chinese version of each English sentence Variety selection | Localization teams | Output in Mandarin or another listed Chinese variety, chosen by tag Baseline testing | Researchers | A reference score to compare other systems against ``` How to set it up: You need these first. - Python with pip - The transformers library, which the model is listed for - A list of the target tags you plan to use, taken from the card 1. Install the Hugging Face hub library with pip. 2. Download the model files with the hf command. 3. Load the model with the transformers library, following the model card. ```sh pip install -U huggingface_hub hf download Helsinki-NLP/opus-mt-en-zh ``` How to use it: Put a target tag in front of every English sentence you send, in the form of two angle brackets around an ID, written as >>id<<. The ID must be one of the thirteen the card lists, such as cmn_Hans, cmn_Hant, yue or wuu. ```sh hf download Helsinki-NLP/opus-mt-en-zh ``` ```table Tool | What it does >>id<< | Sentence-start tag that chooses the Chinese variety to translate into hf download | Fetches the model files to your computer ``` Example: Send the text ">>cmn_Hans<< How are you today?" to the model. It returns the sentence as Chinese text. If you swap the tag for another ID from the card, the output changes to that variety. For newcomers and experts: If you are new, the one thing to remember is the tag at the start of each sentence. Without it the model does not know which Chinese to produce. A first small try is one short sentence with the cmn_Hans tag. If you know the field, this is a one-direction transformer with SentencePiece at 32k and text normalization. It is not multilingual on the source side, which is English only. The card reports a BLEU of 31.4 and a chr-F of 0.268 on Tatoeba-test.eng.zho, with a brevity penalty near 0.9, so outputs run a little shorter than the references. ```flow English sentence Add the >>id<< target tag Normalize and split with SentencePiece Transformer translates Chinese text in the chosen variety ``` Good to know: - Direction: English to Chinese only - Targets: thirteen Chinese variety IDs, including Mandarin, Cantonese and Wu - Required tag: every sentence needs the target tag at its start - Benchmark: BLEU 31.4 and chr-F 0.268 on Tatoeba-test.eng.zho - Other builds: other people have made 7 quantized and 35 fine-tuned versions - License: Apache 2.0 - Cost to run: none named, the source lists no API key, account or paid service - Data it reaches: the text you pass in and the model files on your disk, and the source names no web or account access - Not for: other language pairs or translating without a target tag ## On Hugging Face, 13 November 2026 - Task: translation - Library: transformers - Downloads in the last 30 days: 69,966 - Likes: 411 - License: apache-2.0 - Languages: en, zh - Quantized versions: 7 - Fine-tunes: 35 - Tags: transformers, pytorch, rust, marian, text2text-generation, translation ## Links - Model: https://huggingface.co/Helsinki-NLP/opus-mt-en-zh - Files: https://huggingface.co/Helsinki-NLP/opus-mt-en-zh/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Helsinki-NLP/opus-mt-en-zh ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Helsinki-NLP/opus-mt-en-zh on Hugging Face](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | opus-mt-en-zh (this page) | 69,966 | 411 | — | apache-2.0 | 2023-08-16 | | [SMaLL-100](https://mindket.com/p/00dfd02c-7aa5-4c1d-ae8d-99e914fa16c2) | 10,902 | 113 | 333M | mit | 2024-07-23 | | [t5-base-grammar-correction](https://mindket.com/p/3558b35a-fc7f-4926-ab71-24d4b6b441bd) | 127,257 | 187 | — | cc-by-nc-sa-4.0 | 2022-01-14 | | [Hy-MT2-1.8B](https://mindket.com/p/68c096b8-1b6b-445d-b2b3-ac77722383d1) | 22,844 | 1,285 | 2B | apache-2.0 | 2026-05-26 | | [Index-Translate-2B](https://mindket.com/p/c8b862ee-8c38-4caf-8c2a-31cae2958635) | 1,891 | 81 | 2.3B | apache-2.0 | 2026-10-10 | | [humanizer by jialinyyzz](https://mindket.com/p/10bee27a-389b-4dcd-923e-27f149c67dce) | 391,936 | 2,260 | 12B | apache-2.0 | 2026-10-15 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2023-08-16 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Helsinki-NLP/opus-mt-en-zh on Hugging Face](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/dc2c9808-e987-46c9-8832-291ea8be1508) - [Its page on Mindket](https://mindket.com/p/dc2c9808-e987-46c9-8832-291ea8be1508)