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README.md

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 ---
 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
+<details>
+<summary>Click to expand</summary>
+
+- [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)
+
+</details>
+
+ 
+## 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 <langtech@bsc.es>.
+
+### 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
+
+<details>
+<summary>Click to expand</summary>
+
+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.
+
+</details>