Add config for pruning and sampler

config/imdb_optuna_with_pruning.jsonnet

@@ -0,0 +1,70 @@
+
+local batch_size = 64;
+local cuda_device = 0;
+local num_epochs = 15;
+local seed = 42;
+
+local embedding_dim = std.parseInt(std.extVar('embedding_dim'));
+local dropout = std.parseJson(std.extVar('dropout'));
+local lr = std.parseJson(std.extVar('lr'));
+local max_filter_size = std.parseInt(std.extVar('max_filter_size'));
+local num_filters = std.parseInt(std.extVar('num_filters'));
+local output_dim = std.parseInt(std.extVar('output_dim'));
+local ngram_filter_sizes = std.range(2, max_filter_size);
+
+{
+  numpy_seed: seed,
+  pytorch_seed: seed,
+  random_seed: seed,
+  dataset_reader: {
+    lazy: false,
+    type: 'text_classification_json',
+    tokenizer: {
+      type: 'spacy',
+    },
+    token_indexers: {
+      tokens: {
+        type: 'single_id',
+        lowercase_tokens: true,
+      },
+    },
+  },
+  train_data_path: 'https://s3-us-west-2.amazonaws.com/allennlp/datasets/imdb/train.jsonl',
+  validation_data_path: 'https://s3-us-west-2.amazonaws.com/allennlp/datasets/imdb/dev.jsonl',
+  model: {
+    type: 'basic_classifier',
+    text_field_embedder: {
+      token_embedders: {
+        tokens: {
+          embedding_dim: embedding_dim,
+        },
+      },
+    },
+    seq2vec_encoder: {
+      type: 'cnn',
+      embedding_dim: embedding_dim,
+      ngram_filter_sizes: ngram_filter_sizes,
+      num_filters: num_filters,
+      output_dim: output_dim,
+    },
+    dropout: dropout,
+  },
+  data_loader: {
+    shuffle: true,
+    batch_size: batch_size,
+  },
+  trainer: {
+    cuda_device: cuda_device,
+    epoch_callbacks: [
+      {
+        type: 'optuna_pruner',
+      }
+    ],
+    num_epochs: num_epochs,
+    optimizer: {
+      lr: lr,
+      type: 'sgd',
+    },
+    validation_metric: '+accuracy',
+  },
+}