--- license: apache-2.0 datasets: - projecte-aina/CA-ZH_Parallel_Corpus language: - ca - zh metrics: - bleu library_name: fairseq --- ## Projecte Aina’s Catalan-Chinese machine translation model ## Model description This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-Chinese datasets totalling 6.833.114 sentence pairs. 174.507 sentence pairs were parallel data collected from the web while the remaining 6.658.607 sentence pairs were parallel synthetic data created using the ES-CA translator of [PlanTL](https://huggingface.co/PlanTL-GOB-ES/mt-plantl-es-ca). The model was evaluated on the Flores and NTREX evaluation datasets. ## Intended uses and limitations You can use this model for machine translation from Catalan to simplified Chinese. ## How to use ### Usage Required libraries: ```bash pip install ctranslate2 pyonmttok ``` Translate a sentence using python ```python import ctranslate2 import pyonmttok import re def remove_jieba(text): preserve_spaces = re.sub(r'(?<=[\x00-\x7F])\s(?=[\x00-\x7F])', '@@', text) quit_jieba = re.sub(r'\s', '', preserve_spaces) replace_spaces = re.sub(r'@@', ' ', quit_jieba) return replace_spaces from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-ca-zh", revision="main") tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model") tokenized=tokenizer.tokenize("Benvingut al projecte Aina!") translator = ctranslate2.Translator(model_dir) translated = translator.translate_batch([tokenized[0]], beam_size=10) translation = tokenizer.detokenize(translated[0][0]['tokens']) print(remove_jieba(translation)) ``` ## 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 | |-------------------|----------------| | WikiMatrix | 90.643 | | XLENT | 535.803 | | GNOME | 78| | QED | 3.677 | | TED2020 v1 | 56.269 | | OpenSubtitles | 139.300 | | **Total** | **882.039** | The 6.658.607 sentence pairs of synthetic parallel data were created from the following Spanish-Chinese datasets: | Dataset | Sentences before cleaning | |-------------------|----------------| | UNPC |17.599.223| | CCMatrix | 24.051.233 | | MultiParacrawl| 3.410.087| | **Total** | **45.060.543** | ### Training procedure ### Data preparation The Chinese side of all datasets are passed through the [fastlangid](https://github.com/currentslab/fastlangid) language detector and any sentences which are not identified as simplified Chinese are discarded. The datasets are then also deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). The filtered datasets are then concatenated to form a final corpus of 6.833.114. The Chinese side of the dataset is tokenized using [Jieba](https://github.com/fxsjy/jieba) and before training the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py). #### Tokenization All data is tokenized using sentencepiece, with a 50 thousand token sentencepiece model learned from the combination of all filtered training data. This model is included. #### Hyperparameters The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) The following hyperparameters were set on the Fairseq toolkit: | Hyperparameter | Value | |------------------------------------|----------------------------------| | Architecture | transformer_vaswani_wmt_en_de_big | | Embedding size | 1024 | | Feedforward size | 4096 | | Number of heads | 16 | | Encoder layers | 24 | | Decoder layers | 6 | | Normalize before attention | True | | --share-decoder-input-output-embed | True | | --share-all-embeddings | True | | Effective batch size | 48.000 | | Optimizer | adam | | Adam betas | (0.9, 0.980) | | Clip norm | 0.0 | | Learning rate | 5e-4 | | Lr. schedurer | inverse sqrt | | Warmup updates | 8000 | | Dropout | 0.1 | | Label smoothing | 0.1 | The model was trained for 17.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 4 checkpoints. ## Evaluation ### Variable and metrics We use the BLEU score for evaluation on the [Flores-200](https://github.com/facebookresearch/flores/tree/main/flores200) and [NTREX](https://github.com/MicrosoftTranslator/NTREX) test sets. ### Evaluation results Below are the evaluation results on the machine translation from Catalan to Chinese compared to [Google Translate](https://translate.google.com/), [M2M 1.2B](https://huggingface.co/facebook/m2m100_1.2B) and [ NLLB-200's distilled 1.3B variant](https://huggingface.co/facebook/nllb-200-distilled-1.3B): | Test set | Google Translate | M2M 1.2B | NLLB 1.3B | aina-translator-ca-zh | |----------------------|------------|------------|------------------|---------------| |Flores Dev | **42,6** | 27,8 | 18,9 | 31,4 | |Flores Devtest | **43,7** | 28,4 | 18,4 | 32,6| |NTREX| **36,3** | 24,4 | 14,2 | 26,6| |Average |**41,0** | 26,9| 17,0 | 30,2 | ## 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.