readme: ---
  license: cc-by-4.0
  ---
  ## m2m100 fine-tuned on the ca_zh_wikipedia dataset for machine translation
  
  ## Table of Contents
  
  <details>
  <summary>Click to expand</summary>
    
  - [Model description](#model-description)
  - [Intended uses and limitations](#intended-use)
  - [How to use](#how-to-use)
  - [Training](#training)
    - [Training data](#training-data)
    - [Training procedure](#training-procedure)
      - [Tokenization](#tokenization)
      - [Hyperparameters](#hyperparameters)
  - [Evaluation](#evaluation)
     - [Variable and metrics](#variable-and-metrics)
     - [Evaluation results](#evaluation-results)
  - [Additional information](#additional-information)
    - [Licensing information](#licensing-information)
    - [Funding](#funding)
    - [Disclaimer](#disclaimer)
      
  </details>
    
  ## Model description
  
  This model was obtained by fine-tuning the [m2m100_418M](https://huggingface.co/facebook/m2m100_418M) model on a Ca-Zh machine translation task with the [ca_zh_wikipedia](https://huggingface.co/datasets/projecte-aina/ca_zh_wikipedia) dataset that has been created along with the model. We also evaluate it on a general-domain multilingual testset [Flores-101](https://github.com/facebookresearch/flores).
  
  ## Intended uses and limitations
  
  You can use this model for machine translation from Catalan to Chinese. 
  
  ## How to use
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("projecte-aina/m2m100_418M_ft_zh_ca")
  
  model = AutoModelForSeq2SeqLM.from_pretrained("projecte-aina/m2m100_418M_ft_zh_ca")
  ```
  
  ## Training
  
  ### Training data
  
  As a data for fine-tuning we used the [ca_zh_wikipedia](https://huggingface.co/datasets/projecte-aina/ca_zh_wikipedia) dataset extracted from Wikipedia.
  
  ### Training procedure
  
  #### Tokenization
  
  The original [m2m100_418M](https://huggingface.co/facebook/m2m100_418M) model's sentencepiece tokenizer was used. The fine-tuning dataset that contained both simplified and traditional Chinese was reduced to its simplified form.
  
  #### Hyperparameters
  
  The model was trained for 15 epochs with the default parameters and \\(LR = 2\mathrm{e}{-5}\\).
  
  ## Evaluation
  
  ### Variable and metrics
  
  We use the BLEU score for evaluation on test sets: [Flores-101](https://github.com/facebookresearch/flores).
  
  ### Evaluation results
  
  Below are the evaluation results on the machine translation from Catalan to Chinese compared with the original m2m100 on a testset: [Flores-101](https://github.com/facebookresearch/flores).
  
  |Test set  | Model  | BLEU  |
  | ------------|-------------| -----|
  |Flores-101 | m2m100  |  24.6  | 
  | | m2m100_418M_ft_ca_zh  |  **24.9**  | 
  
  
  ## Additional information
  
  ### Licensing Information
  [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
  
  ### Funding
  This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
  
  ### Citation Information
  ```
  @mastersthesis{MasterThesisZixuanLiu,
    author  = "Zixuan Liu",
    title   = "Improving Chinese-Catalan Machine Translation with Wikipedia Parallel",
    school  = "Universitat Pompeu Fabra",
    year    = 2022,
    address = "Barcelona",
    url= "https://repositori.upf.edu/handle/10230/54142"
  }
  
  @mastersthesis{MasterThesisChenuyeZhou,
    author  = "Chenuye Zhou",
    title   = "Building a Catalan-Chinese parallel corpus for use in MT",
    school  = "Universitat Pompeu Fabra",
    year    = 2022,
    address = "Barcelona",
    url = "https://repositori.upf.edu/handle/10230/54140"
  }
  ```
  
  ### Disclaimer
  <details>
  <summary>Click to expand</summary>
  
  The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
  
  When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their 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 models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
  
  </details>
    
  