text: Title: MultiLabelField not being indexed correctly with pre-trained transformer
  Body: This is probably a user error but I cannot find a jsonl vocab constructor which works correctly with a MultiLabelField (i.e. a multi-label classifier).
  
  I need to set the vocabs `unk` and `pad` token as I'm using a huggingface transformer, and of course, I need to index the labels.
  
  When I use `from_pretrained_transformer` to construct my vocabulary there are two issues, first, when `MultiLabelField.index` is called, the vocab only contains a tokens namespace, no labels. This causes 'index' to crash - oddly `vocab.get_token_index(label, self._label_namespace)` returns 1 (one) for every label despite the namespace not existing, should it not return an error?
  
      vocabulary: {
          type: "from_pretrained_transformer",
          model_name: "models/transformer",
      }
  
  Also inspecting the vocab object I'm seeing
  
  _oov_token:'\<unk\>'
  _padding_token:'@@PADDING@@'
  
  So it's failed to infer the padding token. From what I can see the from_pretrained_transformer has no `padding_token` argument?
  
  If I use 'from_instances' it indexes the labels correctly but afaik it's reindexing the original vocab but it's out of alignment.
  
  My model is
  
      vocabulary: {
          type: "from_pretrained_transformer",
          model_name: "models/transformer",
      },
      dataset_reader: {
          type: "multi_label",
          tokenizer: {
            type: "pretrained_transformer",
            model_name: "models/transformer"
          },
          token_indexers: {
              tokens: {
                  type: "pretrained_transformer",
                  model_name: "models/transformer",
                  namespace: "tokens" 
              },
          },
      },
      model: {
          type: "multi_label",
          text_field_embedder: {
              token_embedders: {
                  tokens: {
                      type: "pretrained_transformer",
                      model_name: "models/transformer"
                  }
              },
          },
          seq2vec_encoder: {
              type: "bert_pooler",
              pretrained_model: "models/transformer",
              dropout: 0.1,
          },
      },
  
  