README.md

metadata

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
• How to Get Started With the Model
• Uses
• Risks, Limitations and Biases
• Training

Model Details

Model Description: This model is a fine-tuned checkpoint of RoBERTA-base, fine-tuned for Track 1 of theWASSA 2022 Shared Task - 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

Fine-tuning hyper-parameters

• learning_rate = 1e-5
• batch_size = 32
• warmup = 600
• max_seq_length = 128
• num_train_epochs = 3.0