Update code fences in getting started tutorial
@@ -455,7 +455,7 @@ lstm = PytorchSeq2SeqWrapper(torch.nn.LSTM(EMBEDDING_DIM, HIDDEN_DIM, batch_firs
You could write
-```
+```python
lstm_params = Params({
"type": "lstm",
"input_size": EMBEDDING_DIM,
@@ -589,7 +589,7 @@ The first big change is that we need to add a second `TokenIndexer` to our datas
that captures the character-level indices. As the token indexers can be provided as a
constructor input, we can accomplish this with just a small change to our configuration file:
-```json
+```jsonnet
"dataset_reader": {
"type": "pos-tutorial",
"token_indexers": {
@@ -613,7 +613,7 @@ when we specify token embedders inside our model.
The section of the configuration corresponding to our model was previously
-```json
+```jsonnet
"model": {
"type": "lstm-tagger",
"word_embeddings": {
@@ -636,7 +636,7 @@ The `BasicTokenEmbedder` already knows that if it has multiple token embedders i
should concatenate their outputs. So all we need to do is add a second token embedder
with the right key:
-```json
+```jsonnet
"token_embedders": {
"tokens": {
"type": "embedding",
