update readme with ref to dataset

README.md

@@ -6,6 +6,11 @@ language:
 base_model:
 - distilbert/distilbert-base-multilingual-cased
 library_name: transformers
+datasets:
+- sjhuskey/latin_author_dll_id
+metrics:
+- f1
+- accuracy
 ---
 
 # DLL Catalog Author Reconciliation Model
@@ -26,4 +31,4 @@ Achieving accuracy and reliability in this process will make the second goal of
 
 ## The Model
 
-After preliminary experiments with sequential neural network models using [bag-of-words](https://en.wikipedia.org/wiki/Bag-of-words_model), [term frequency-inverse document frequency](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) (tf-idf), and custom word embedding encoding, I settled on using a pretrained BERT model developed by [Devlin et al. 2018](https://arxiv.org/abs/1810.04805v2). Specifically, I'm using [Hugging Face's DistilBert base multilingual (cased) model](https://huggingface.co/distilbert/distilbert-base-multilingual-cased), which is based on work by [Sanh et al. 2020](https://doi.org/10.48550/arXiv.1910.01108).
+After preliminary experiments with sequential neural network models using [bag-of-words](https://en.wikipedia.org/wiki/Bag-of-words_model), [term frequency-inverse document frequency](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) (tf-idf), and custom word embedding encoding, I settled on using a pretrained BERT model developed by [Devlin et al. 2018](https://arxiv.org/abs/1810.04805v2). Specifically, I'm using [Hugging Face's DistilBert base multilingual (cased) model](https://huggingface.co/distilbert/distilbert-base-multilingual-cased), which is based on work by [Sanh et al. 2020](https://doi.org/10.48550/arXiv.1910.01108).