|
--- |
|
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 |
|
|
|
|
|
|
