AllenNLP configuration

Original configuration

Here is the example of AllenNLP configuration.

• imdb.jsonnet

local batch_size = 64;
local cuda_device = -1;
local num_epochs = 15;
local seed = 42;


local embedding_dim = 32;
local dropout = 0.5;
local lr = 1e-3;
local max_filter_size = 5;
local num_filters = 32;
local output_dim = 64;
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,
      },
    },
  },
  datasets_for_vocab_creation: ['train'],
  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,
    num_epochs: num_epochs,
    optimizer: {
      lr: lr,
      type: 'sgd',
    },
    validation_metric: '+accuracy',
  },
}

Setup for allennlp-optuna

You have to change values of hyperparameter that you want to optimize. For example, if you want to optimize a dimensionality of word embedding, a change should be following:

< local embedding_dim = 32;
---
> local embedding_dim = std.parseInt(std.extVar('embedding_dim'));

The sample configuration looks like following:

• imdb_optuna.jsonnet

local batch_size = 64;
local cuda_device = -1;
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,
      },
    },
  },
  datasets_for_vocab_creation: ['train'],
  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,
    num_epochs: num_epochs,
    optimizer: {
      lr: lr,
      type: 'sgd',
    },
    validation_metric: '+accuracy',
  },
}

Well done, you have completed the setup AllenNLP configuration for allennlp-optuna.