parser.jsonnet

{
    dataset_reader:{
        type:"universal_dependencies",
        use_language_specific_pos: true,
        token_indexers: {
            tokens: {
                type: 'pretrained_transformer_mismatched',
                model_name: "bert-base-multilingual-cased",
                max_length: 512,
            }
        }
    },
    train_data_path: "data/English/Parser/train.conllu",
    validation_data_path: "data/English/Parser/dev.conllu",
    model: {
      type: "biaffine_parser",
      text_field_embedder: {
        token_embedders: {
            tokens: {
                type: 'pretrained_transformer_mismatched',
                model_name: "bert-base-multilingual-cased",
                max_length: 512,
                gradient_checkpointing: true,
            },
        },
      },
      pos_tag_embedding:{
        embedding_dim: 100,
        vocab_namespace: "pos",
        sparse: true
      },
      encoder: {
        type: "stacked_bidirectional_lstm",
        input_size: 868,
        hidden_size: 400,
        num_layers: 3,
        recurrent_dropout_probability: 0.3,
        use_highway: true
      },
      use_mst_decoding_for_validation: true,
      arc_representation_dim: 500,
      tag_representation_dim: 100,
      dropout: 0.3,
      input_dropout: 0.3,
      initializer: {
        regexes: [
          [".*projection.*weight", {type: "xavier_uniform"}],
          [".*projection.*bias", {type: "zero"}],
          [".*tag_bilinear.*weight", {type: "xavier_uniform"}],
          [".*tag_bilinear.*bias", {type: "zero"}],
          [".*weight_ih.*", {type: "xavier_uniform"}],
          [".*weight_hh.*", {type: "orthogonal"}],
          [".*bias_ih.*", {type: "zero"}],
          [".*bias_hh.*", {type: "lstm_hidden_bias"}]
        ]
      }
    },
    data_loader: {
      batch_sampler: {
        type: "bucket",
        batch_size : 10
      }
    },
    trainer: {
      cuda_device: 0,
      num_epochs: 80,
      grad_norm: 5.0,
      patience: 10,
      validation_metric: "+LAS",
      optimizer: {
        type: "dense_sparse_adam",
        betas: [0.9, 0.9]
      }
    }
}