Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+datasets:
+- sst2
+- glue
+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
+      verified: true
+    - name: Pearson's R (empathy)
+      type: Pearson's R (empathy)
+      value: 0.4336383660597612
+      verified: true
+    - name: Pearson's R (distress)
+      type: Pearson's R (distress)
+      value: 0.40006974689041663
+      verified: true
+
+---
+
+# 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
+
+