Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- kortukov/answer-equivalence-dataset
+language:
+- en
+pipeline_tag: text-classification
 ---
+
+# Overview
+BEM - BERT Matching model from paper [Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation](https://arhttps://arxiv.org/abs/2202.07654xiv.org/abs/2202.07654) (reproduction).
+
+It is a [bert-base-uncased](https://huggingface.co/bert-base-uncased) model trained on the [Answer Equivalence dataset](https://huggingface.co/datasets/kortukov/answer-equivalence-dataset)
+
+Consider this example (pseudocode):
+```python
+question = 'how is the weather in california'
+reference answer = 'infrequent rain'
+candidate answer = 'rain'
+bem(question, reference, candidate) ~ 0
+```
+
+This model can be used as a metric to evaluate automatic question answering systems: when the produced answer is different from the reference, it might still be equivalent to the reference and hence count as correct.
+
+See the paper [Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation](https://arxiv.org/abs/2202.07654) for a detailed explanation of how the data was collected and how this metric compares to others such as exact match of F1.
+
+# Example use
+
+TODO
+