readme: ---
  language: en
  license: apache-2.0
  datasets:
  - empathic reactions to news stories
  model-index:
  - name: roberta-base-empathy
    results:
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: Reaction to News Stories
        type: Reaction to News Stories
        config: sst2
        split: validation
      metrics:
      - name: MSE loss
        type: MSE loss
        value: 7.07853364944458
      - name: Pearson's R (empathy)
        type: Pearson's R (empathy)
        value: 0.4336383660597612
      - name: Pearson's R (distress)
        type: Pearson's R (distress)
        value: 0.40006974689041663
  
  ---
  
  # Roberta base finetuned on a dataset of empathic reactions to news stories (Buechel et al., 2018; Tafreshi et al., 2021, 2022)
  
  ## Table of Contents
  - [Model Details](#model-details)
  - [How to Get Started With the Model](#how-to-get-started-with-the-model)
  - [Uses](#uses)
  - [Risks, Limitations and Biases](#risks-limitations-and-biases)
  - [Training](#training)
  
  ## Model Details
  **Model Description:** This model is a fine-tuned checkpoint of [RoBERTA-base](https://huggingface.co/roberta-base), fine-tuned for Track 1 of the[WASSA 2022 Shared Task](https://aclanthology.org/2022.wassa-1.20.pdf) - predicting empathy and distress scores on a dataset of reactions to news stories.
  This model attained an average Pearson's correlation (r) of 0.416854 on the dev set (for comparison, the top team had an average r of .54 on the test set ).
  
  # Training
  
  #### Training Data
  An extended version of the [empathic reactions to news stories dataset](https://codalab.lisn.upsaclay.fr/competitions/834#learn_the_details-datasets)
  
  ###### Fine-tuning hyper-parameters
  
  
  - learning_rate = 1e-5
  - batch_size = 32
  - warmup = 600
  - max_seq_length = 128
  - num_train_epochs = 3.0
  
  
  