code: def write_model_card(model_card_dir, src_lang, tgt_lang):
      texts = {
          "en": "Machine learning is great, isn't it?",
          "ru": "Машинное обучение - это здорово, не так ли?",
          "de": "Maschinelles Lernen ist großartig, oder?",
      }
  
      # BLUE scores as follows:
      # "pair": [fairseq, transformers]
      scores = {
          "ru-en": [
              "[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)",
              "39.20",
          ],
          "en-ru": [
              "[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)",
              "33.47",
          ],
          "en-de": [
              "[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)",
              "42.83",
          ],
          "de-en": [
              "[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)",
              "41.35",
          ],
      }
      pair = f"{src_lang}-{tgt_lang}"
  
      readme = f"""
  ---
  language:
  - {src_lang}
  - {tgt_lang}
  thumbnail:
  tags:
  - translation
  - wmt19
  - facebook
  license: apache-2.0
  datasets:
  - wmt19
  metrics:
  - bleu
  ---
  
  # FSMT
  
  ## Model description
  
  This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
  
  For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
  
  The abbreviation FSMT stands for FairSeqMachineTranslation
  
  All four models are available:
  
  * [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
  * [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
  * [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
  * [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
  
  ## Intended uses & limitations
  
  #### How to use
  
  ```python
  from transformers import FSMTForConditionalGeneration, FSMTTokenizer
  mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
  tokenizer = FSMTTokenizer.from_pretrained(mname)
  model = FSMTForConditionalGeneration.from_pretrained(mname)
  
  input = "{texts[src_lang]}"
  input_ids = tokenizer.encode(input, return_tensors="pt")
  outputs = model.generate(input_ids)
  decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
  print(decoded) # {texts[tgt_lang]}
  
  ```
  
  #### Limitations and bias
  
  - The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
  
  ## Training data
  
  Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
  
  ## Eval results
  
  pair   | fairseq | transformers
  -------|---------|----------
  {pair}  | {scores[pair][0]} | {scores[pair][1]}
  
  The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
  - model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
  - re-ranking
  
  The score was calculated using this code:
  
  ```bash
  git clone https://github.com/huggingface/transformers
  cd transformers
  export PAIR={pair}
  export DATA_DIR=data/$PAIR
  export SAVE_DIR=data/$PAIR
  export BS=8
  export NUM_BEAMS=15
  mkdir -p $DATA_DIR
  sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
  sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
  echo $PAIR
  PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
  ```
  note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
  
  ## Data Sources
  
  - [training, etc.](http://www.statmt.org/wmt19/)
  - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
  
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @inproceedings{{...,
    year={{2020}},
    title={{Facebook FAIR's WMT19 News Translation Task Submission}},
    author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
    booktitle={{Proc. of WMT}},
  }}
  ```
  
  
  ## TODO
  
  - port model ensemble (fairseq uses 4 model checkpoints)
  
  """
      os.makedirs(model_card_dir, exist_ok=True)
      path = os.path.join(model_card_dir, "README.md")
      print(f"Generating {path}")
      with open(path, "w", encoding="utf-8") as f:
          f.write(readme)
  
label_desc: Use image node_id 1 for calling a global function with example usage: write_model_card(model_card_dir, src_lang, tgt_lang) without return types