Fix typo

README.md

@@ -7,13 +7,13 @@ datasets:
 
 # NT5, a T5 model trained to perform numerical reasoning
 
-T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on DROP. It was introduced in the paper [NT5?! Training T5 to Perform Numerical Reasoning](https://arxiv.org/abs/2104.07307) by Yang et al. and first released in [this repository](https://github.com/lesterpjy/numeric-t5). As the original implementation was in Tensorflow 2, I've converted the weigths to PyTorch. This model corresponds to RC Experiment 1 (see the paper), their best performing model.
+T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on [DROP](https://allennlp.org/drop.html). It was introduced in the paper [NT5?! Training T5 to Perform Numerical Reasoning](https://arxiv.org/abs/2104.07307) by Yang et al. and first released in [this repository](https://github.com/lesterpjy/numeric-t5). As the original implementation was in Tensorflow 2, I've converted the weigths to PyTorch. This model corresponds to RC Experiment 1 (see the paper), their best performing model.
 
 Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me.
 
 ## Model description
 
-The NT5 model is a T5 model, in other words, an encoder-decoder Transformer. In order to encourage numerical reasoning, the model was further pre-trained on three datasets designed to strengthen skills necessary for numerical reasoning over text (NRoT) and general reading comprehension before being fine-tuned on Discrete Reasoning over Text (DROP) dataset.
+The NT5 model is a T5 model, in other words, an encoder-decoder Transformer. In order to encourage numerical reasoning, the model was further pre-trained on three datasets designed to strengthen skills necessary for numerical reasoning over text (NRoT) and general reading comprehension before being fine-tuned on the Discrete Reasoning over Text (DROP) dataset.
 
 ## Intended uses & limitations
 
@@ -99,6 +99,6 @@ This model achieves an F1 score of 0.7031 and exact match of 0.6687 on the devel
   biburl    = {https://dblp.org/rec/journals/corr/abs-1903-00161.bib},
   bibsource = {dblp computer science bibliography, https://dblp.org}
 }
-a service of Schloss Dagstuhl - Leibniz Center for Informatics\\thomebrowsesearchabout
+a service of Schloss Dagstuhl - Leibniz Center for Informatics\\\\thomebrowsesearchabout
 
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