readme: ---
  language:
  - es
  tags:
  - sentence similarity  # Example: audio
  - passage reranking  # Example: automatic-speech-recognition
  datasets:
  - IIC/msmarco_es
  
  metrics:
  - eval_MRR@10: 0.688
  
  model-index:
  - name: roberta-base-bne-ranker
    results:
    - task: 
        type: text similarity  # Required. Example: automatic-speech-recognition
        name: text similarity  # Optional. Example: Speech Recognition
      dataset:
        type: IIC/msmarco_es  # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
        name: IIC/msmarco_es  # Required. Example: Common Voice zh-CN
        args: es         # Optional. Example: zh-CN
      metrics:
        - type: MRR@10
          value: 0.688
          name: eval_MRR@10
  ---
  
  This is a model to rank documents based on importance. It is trained on an [automatically translated version of MS Marco](https://huggingface.co/datasets/IIC/msmarco_es). After some experiments, the best configuration was to train for 2 epochs with learning rate 2e-5 and batch size 32. 
  
  Example of use:
  
  ```python
  from sentence_transformers import CrossEncoder
  
  model = CrossEncoder("IIC/roberta-base-bne-ranker", device="cpu")
  
  question = "¿Cómo se llama el rey?"
  
  contexts = ["Me encanta la canción de el rey", "Cuando el rey fue a Sevilla, perdió su silla", "El rey se llama Juan Carlos y es conocido por sus escándalos"]
  
  similarity_scores = model.predict([[question, context] for context in contexts])
  ```
  
  ### 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.