--- license: apache-2.0 datasets: - projecte-aina/CA-ZH_Parallel_Corpus language: - zh - ca base_model: - facebook/m2m100_1.2B --- ## Projecte Aina’s Chinese-Catalan machine translation model ## Table of Contents
Click to expand - [Model description](#model-description) - [Intended uses and limitations](#intended-uses-and-limitations) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Evaluation](#evaluation) - [Additional information](#additional-information)
## Model description This machine translation model is built upon the foundation of M2M100 1.2B. It is trained on a combination of Catalan-Chinese datasets totalling 94.187.858 sentence pairs. 113.305 sentence pairs were parallel data collected from the web, while the remaining 94.074.553 sentence pairs were parallel synthetic data created using the [Aina Project's Spanish-Catalan machine translation model](https://huggingface.co/projecte-aina/aina-translator-es-ca) and the [Aina Project's English-Catalan machine translation model](https://huggingface.co/projecte-aina/aina-translator-en-ca). The model was evaluated on the Flores, NTREX, and Projecte Aina's Catalan-Chinese evaluation datasets. ## Intended uses and limitations You can use this model for machine translation from simplified Chinese to Catalan. ## How to use ### Usage Translate a sentence using python ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM model_id = "projecte-aina/aina-translator-zh-ca-v2" model = AutoModelForSeq2SeqLM.from_pretrained(model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) sentence = "欢迎来到 Aina 项目！" input_ids = tokenizer(sentence, return_tensors="pt").input_ids output_ids = model.generate(input_ids, max_length=200, num_beams=5) generated_translation= tokenizer.decode(output_ids[0], skip_special_tokens=True).strip() print(generated_translation) #Benvingut al projecte Aina! ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Training data The Catalan-Chinese data collected from the web was a combination of the following datasets: | Dataset | Sentences before cleaning | |-------------------|----------------| | OpenSubtitles | 139.300 | | WikiMatrix | 90.643 | | Wikipedia | 68.623| | **Total** | **298.566** | 94.074.553 sentence pairs of synthetic parallel data were created from the following Spanish-Chinese datasets and English-Chinese datasets: **Spanish-Chinese:** | Dataset | Sentences before cleaning | |-------------------|----------------| | NLLB |24.051.233| | UNPC | 17.599.223 | | MultiUN | 9.847.770 | | OpenSubtitles | 9.319.658 | | MultiParaCrawl | 3.410.087 | | MultiCCAligned | 3.006.694 | | WikiMatrix | 1.214.322 | | News Commentary | 375.982 | | Tatoeba | 9.404 | | **Total** | **68.834.373** | **English-Chinese:** | Dataset | Sentences before cleaning | |-------------------|----------------| | NLLB |71.383.325| | CCAligned | 15.181.415 | | Paracrawl | 14.170.869| | WikiMatrix | 2.595.119| | **Total** | **103.330.728** | ### Training procedure ### Data preparation The Chinese side of all datasets were first processed using the [Hanzi Identifier](https://github.com/tsroten/hanzidentifier) to detect Traditional Chinese, which was subsequently converted to Simplified Chinese using [OpenCC](https://github.com/BYVoid/OpenCC). All data was then filtered according to two specific criteria: - Alignment: sentence level alignments were calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) and sentence pairs with a score below 0.75 were discarded. - Language identification: the probability of being the target language was calculated using [Lingua.py](https://github.com/pemistahl/lingua-py) and sentences with a language probability score below 0.5 were discarded. Next, Spanish data was translated into Catalan using the Aina Project's [Spanish-Catalan machine translation model](https://huggingface.co/projecte-aina/aina-translator-es-ca), while English data was translated into Catalan using the Aina Project's [English-Catalan machine translation model](https://huggingface.co/projecte-aina/aina-translator-en-ca). The filtered and translated datasets are then concatenated and deduplicated to form a final corpus of 94.187.858. #### Training The training was executed on NVIDIA GPUs utilizing the Hugging Face Transformers framework. The model was trained for 244.500 updates. Weights were saved every 500 updates. ## Evaluation ### Variable and metrics Below are the evaluation results on [Flores-200](https://github.com/facebookresearch/flores/tree/main/flores200), [NTREX](https://github.com/MicrosoftTranslator/NTREX), and Projecte Aina's Catalan-Chinese test sets, compared to Google Translate for the ZH-CA direction. The evaluation was conducted [`tower-eval`](https://github.com/deep-spin/tower-eval) following the standard setting (beam search with beam size 5, limiting the translation length to 200 tokens). We report the following metrics: - BLEU: Sacrebleu implementation, version:2.4.0. - ChrF: Sacrebleu implementation. - Comet: Model checkpoint: "Unbabel/wmt22-comet-da". - Comet-kiwi: Model checkpoint: "Unbabel/wmt22-cometkiwi-da". ### Evaluation results Below are the evaluation results on the machine translation from Chinese to Catalan compared to [Google Translate](https://translate.google.com/): #### Flores200-dev | | Bleu ↑ | ChrF ↑ | Comet ↑ | Comet-kiwi ↑ | |:-----------------------|-------:|------:|-------:|--------:|-------------:|---------:| | aina-translator-zh-ca-v2 | 26.74 | 54.49 | **0.86** | **0.82** | | Google Translate | **27.71** | **55.37** | **0.86** | 0.81 | #### Flores200-devtest | | Bleu ↑ | ChrF ↑ | Comet ↑ | Comet-kiwi ↑ | |:-----------------------|-------:|------:|-------:|--------:|-------------:|---------:| | aina-translator-zh-ca-v2 | 27.17 | 55.02 | **0.86** | **0.81** | | Google Translate | **27.47** | **55.51** | **0.86** | **0.81** | #### NTREX | | Bleu ↑ | ChrF ↑ | Comet ↑ | Comet-kiwi ↑ | |:-----------------------|-------:|------:|-------:|--------:|-------------:|---------:| | aina-translator-zh-ca-v2 | 22.43 | 50.65 | **0.83** | **0.79** | | Google Translate | **23.49** | **51.29** | **0.83** | **0.79** | #### Projecte Aina's Catalan-Chinese evaluation dataset | | Bleu ↑ | ChrF ↑ | Comet ↑ | Comet-kiwi ↑ | |:-----------------------|-------:|------:|-------:|--------:|-------------:|---------:| | aina-translator-zh-ca-v2 | **29.21** | 57.41 | **0.87** | **0.82** | | Google Translate | 28.86 | **57.73** | **0.87** | **0.82** | ## Additional information ### Author The Language Technologies Unit from Barcelona Supercomputing Center. ### Contact For further information, please send an email to . ### Copyright Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center. ### License [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/). ### Disclaimer
Click to expand The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0. Be aware that the model may have biases and/or any other undesirable distortions. When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the model (Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties.