readme: ---
  license: cc-by-4.0
  language:
  - ca
  - de
  - multilingual
  datasets:
  - Softcatala/parallel-catalan-corpus/deu-cat
  metrics:
  - "bleu"
  - "meteor"
  - "chrf"
  - "ter"
  model-index:
  - name: m2m100_418M_ft_de_ca
    results:
    - task:
        type: translation 
      dataset:
        type: flores
        name: Flores
      metrics:
        - name: BLEU
          type: bleu
          value: 28.5
    - task:
        type: translation 
      dataset:
        type: wmt/wmt13
        name: WMT13
      metrics:
        - name: BLEU
          type: bleu
          value: 22.9
    - task:
        type: translation 
      dataset:
        type: flores
        name: Flores
      metrics:
        - name: TER
          type: ter
          value: 60.7
    - task:
        type: translation 
      dataset:
        type: wmt/wmt13
        name: WMT13
      metrics:
        - name: TER
          type: ter
          value: 71.0
    - task:
        type: translation 
      dataset:
        type: flores
        name: Flores
      metrics:
        - name: METEOR
          type: meteor
          value: 55.9
    - task:
        type: translation 
      dataset:
        type: wmt/wmt13
        name: WMT13
      metrics:
        - name: METEOR
          type: meteor
          value: 49.5
    - task:
        type: translation 
      dataset:
        type: flores
        name: Flores
      metrics:
        - name: chrF
          type: chrf
          value: 55.9
    - task:
        type: translation 
      dataset:
        type: wmt/wmt13
        name: WMT13
      metrics:
        - name: chrF
          type: chrf
          value: 54.1
  ---
  ## m2m100 fine-tuned on Softcatalà's parallel Catalan-German 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 De-Ca machine translation task with the [Softcatalà Catalan-German parallel corpus](https://github.com/Softcatala/parallel-catalan-corpus/tree/master/deu-cat) dataset, with sentences deduplicated and filtered by the [GEnCaTa quality filter](https://huggingface.co/projecte-aina/mbert-base-gencata). We also evaluate it on a general-domain multilingual testset [Flores-200](https://github.com/facebookresearch/flores) and [WMT13](https://www.statmt.org/wmt13/).
  
  ## Intended uses and limitations
  
  You can use this model for machine translation from German to Catalan.
  
  ## How to use
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("projecte-aina/2m100-418M-ft-de-ca")
  
  model = AutoModelForSeq2SeqLM.from_pretrained("projecte-aina/2m100-418M-ft-de-ca")
  ```
  
  ## Training
  
  ### Training data
  
  As a data for fine-tuning we used the [Softcatalà Catalan-German parallel corpus](https://github.com/Softcatala/parallel-catalan-corpus/tree/master/deu-cat) dataset, with sentences deduplicated and filtered by the [GEnCaTa quality filter](https://huggingface.co/projecte-aina/mbert-base-gencata).
  
  ### Training procedure
  
  #### Tokenization
  
  The original [m2m100_418M](https://huggingface.co/facebook/m2m100_418M) model's sentencepiece tokenizer was used.
  
  #### Hyperparameters
  
  The model was trained for 2 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-200](https://github.com/facebookresearch/flores) and [WMT13](https://www.statmt.org/wmt13/).
  
  ### Evaluation results
  
  Below are the evaluation results on the machine translation from German to Catalan compared with the original m2m100 on a testset: [Flores-200](https://github.com/facebookresearch/flores).
  
  |Test set  | Model  | BLEU  | TER | METEOR | chrF
  | ------------|-------------| -----| -----| -----| -----|
  |Flores-200 | m2m100  | 26.6 | 63.1 | 54.0 | 53.5 |
  | | m2m100-418M-ft-de-ca  |  **28.5**  |  **60.7** | **55.9** | **55.9** |
  |WMT13 | m2m100  | 21.8 | 72.8 | 48.0 | 53.5 |
  | | m2m100-418M-ft-de-ca  |  **22.9**  |  **71.0** | **49.5** | **54.1** |
  
  ## 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.c