readme: ---
  language:
  - es
  tags:
  # - summarization  # Example: audio
  - seq2seq  # Example: automatic-speech-recognition
  - abstractive question answering
  datasets:
  - IIC/lfqa_es
  metrics:
  - rouge2
  - rouge1
  - rougel
  - rougelsum
  
  # Optional. Add this if you want to encode your eval results in a structured way.
  model-index:
  - name: mt5-base-lfqa-es
    results:
    - task: 
        type: question answering # Required. Example: automatic-speech-recognition
        name: abstractive question answering  # Optional. Example: Speech Recognition
      dataset:
        type: IIC/lfqa_es # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
        name: IIC/lfqa_es  # Required. Example: Common Voice zh-CN
        args: es         # Optional. Example: zh-CN
      metrics:
        - type: rouge1    # Required. Example: wer
          value: 10.2907  # Required. Example: 20.90
          name: rouge1    # Optional. Example: Test WER
        - type: rouge2
          value: 1.7251
          name: rouge2
        - type: rougeL
          value: 8.9193
          name: rougeL
        - type: rougeLsum
          value: 7.9875
          name: rougeLsum
  ---
  
  This model is a fine-tuned version of [MT5-base](https://huggingface.co/google/mt5-base), a multilingual text-to-text encoder-decoder transformer. It is trained on [lfqa-spanish](https://huggingface.co/datasets/IIC/lfqa_spanish), an automatically translated dataset, originally created in English in [this repository](https://huggingface.co/vblagoje/bart_lfqa). For more details about the dataset, check its model card. 
  
  We used linear decay, and the full hyperparameters for this model were:
  
  ```json
  {
    "learning_rate": 2e-4,
    "num_train_epochs": 3,
    "adam_beta1": 0.9,
    "adam_beta2": 0.999,
    "adam_epsilon": 1e-8,
    "total_train_batch_size": 64,
    "warmup_ratio": 0.06,
  }
  ```
  
  This model is therefore trained to provide long-form answers to open domain questions given certain context paragraphs which can be used to answer that question. Therefore the main task this model can perform is abstractive question answering.
  
  The result it obtains on the validation set of this dataset (it doesn't have a test set), with num_beams = 8 and maximum target sequence length = 360 are:
  
  ```json
  {"rouge1": 10.2907, "rouge2": 1.7251, "rougeL": 8.9193, "rougeLsum": 7.9875, "gen_len": 296.258}
  ```
  
  ### Contributions
  Thanks to [@avacaondata](https://huggingface.co/avacaondata), [@alborotis](https://huggingface.co/alborotis), [@albarji](https://huggingface.co/albarji), [@Dabs](https://huggingface.co/Dabs), [@GuillemGSubies](https://huggingface.co/GuillemGSubies) for adding this model.