Update README.md

README.md

@@ -15,7 +15,7 @@ metrics:
 
 Attention-based RNN sequence-to-sequence model for [Timers and Such](https://zenodo.org/record/4623772) trained on the `train-real` subset. This model checkpoint achieves 86.7% accuracy on `test-real`.
 
-The model uses an ASR model trained on LibriSpeech (`speechbrain/asr-crdnn-rnnlm-librispeech`) to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. 
+The model uses an ASR model trained on LibriSpeech ([`speechbrain/asr-crdnn-rnnlm-librispeech`](https://huggingface.co/speechbrain/asr-crdnn-rnnlm-librispeech)) to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. 
 
 The dataset has four intents: `SetTimer`, `SetAlarm`, `SimpleMath`, and `UnitConversion`. Try testing the model by saying something like "set a timer for 5 minutes" or "what's 32 degrees Celsius in Fahrenheit?"
 
@@ -35,7 +35,7 @@ title = {SpeechBrain},
 year = {2021},
 publisher = {GitHub},
 journal = {GitHub repository},
-howpublished = {\\\\\\\\\\\\\\\\url{https://github.com/speechbrain/speechbrain}},
+howpublished = {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\url{https://github.com/speechbrain/speechbrain}},
 }
 ```