ner_collection3_m1.json

{
  "dataset_reader": {
    "class_name": "conll2003_reader",
    "data_path": "{DOWNLOADS_PATH}/collection3/",
    "dataset_name": "collection3",
    "provide_pos": false,
    "provide_chunk": false,
    "iobes": true
  },
  "dataset_iterator": {
    "class_name": "data_learning_iterator"
  },
  "chainer": {
    "in": ["x"],
    "in_y": ["y"],
    "pipe": [
      {
        "in": ["x"],
        "out": ["x_tokens"],
        "class_name": "lazy_tokenizer"
      },
      {
        "in": ["x_tokens"],
        "out": ["x_lower", "sent_lengths", "x_tokens_elmo"],
        "class_name": "ner_preprocessor",
        "id": "ner_preprocessor",
        "get_x_padded_for_elmo": true,
        "get_x_cap_padded": false
      },
      {
        "in": ["x_lower"],
        "out": ["x_tok_ind"],
        "fit_on": ["x_lower"],
        "class_name": "ner_vocab",
        "id": "word_vocab",
        "save_path": "{MODELS_PATH}/word.dict",
        "load_path": "{MODELS_PATH}/word.dict"
      },
      {
        "in": ["y"],
        "out": ["y_ind"],
        "fit_on": ["y"],
        "class_name": "ner_vocab",
        "id": "tag_vocab",
        "save_path": "{MODELS_PATH}/tag.dict",
        "load_path": "{MODELS_PATH}/tag.dict"
      },
      {
        "in": ["x_tokens"],
        "out": ["x_char_ind"],
        "fit_on": ["x_tokens"],
        "class_name": "ner_vocab",
        "char_level": true,
        "id": "char_vocab",
        "save_path": "{MODELS_PATH}/char.dict",
        "load_path": "{MODELS_PATH}/char.dict"
      },
      {
        "in":[
          "sent_lengths",
          "x_tok_ind",
          "x_char_ind",
          "x_tokens_elmo"
        ],
        "in_y": ["y_ind"],
        "out": ["y_predicted"],
        "class_name": "hybrid_ner_model",
        "n_tags": "#tag_vocab.len",
        "word_emb_path": "{DOWNLOADS_PATH}/embeddings/lenta_lower_100.bin",
        "word_emb_name": "fasttext",
        "word_dim": 100,
        "word_vocab": "#word_vocab",        
        "char_vocab_size": "#char_vocab.len",
        "char_dim": 100,
        "elmo_dim": 128,
        "elmo_hub_path": "http://files.deeppavlov.ai/deeppavlov_data/elmo_ru-news_wmt11-16_1.5M_steps.tar.gz",
        "lstm_hidden_size": 256,
        "save_path": "{MODELS_PATH}/collection3",
        "load_path": "{MODELS_PATH}/collection3",
        "learning_rate": 1e-3,
        "learning_rate_drop_patience": 5,
        "learning_rate_drop_div": 10,
        "dropout_keep_prob": 0.7
      },
      {
        "in": ["y_predicted"],
        "out": ["tags"],
        "class_name": "convert_ids2tags",
        "id2tag": "#tag_vocab.i2t"
      }
    ],
    "out": ["x_tokens", "tags"]
  },
  "train": {
    "epochs": 100,
    "batch_size": 64,
    "metrics": [
      {
        "name": "ner_f1",
        "inputs": ["y", "tags"]
      },
      {
        "name": "ner_token_f1",
        "inputs": ["y", "tags"]
      }
    ],
    "validation_patience": 10,
    "val_every_n_epochs": 1,
    "log_every_n_epochs": 1,
    "show_examples": false,
    "class_name": "nn_trainer",
    "evaluation_targets": [
      "valid",
      "test"
    ]
  },
  "metadata": {
    "variables": {
      "ROOT_PATH": "~/.deeppavlov",
      "DOWNLOADS_PATH": "{ROOT_PATH}/downloads",
      "MODELS_PATH": "{ROOT_PATH}/models/collection3"
    },
    "download": [
      {
        "url": "http://files.deeppavlov.ai/deeppavlov_data/ner_collection3_m1.tar.gz",
        "subdir": "{MODELS_PATH}"
      },
      {
        "url": "http://files.deeppavlov.ai/embeddings/lenta_lower_100.bin",
        "subdir": "{DOWNLOADS_PATH}/embeddings"
      }
    ]
  }
}