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matchlstm_graph.json
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matchlstm_graph.json
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{
"dataset_reader": {
"type": "scitail_graph",
"token_indexers": {
"tokens": {
"type": "single_id",
"lowercase_tokens": true
}
},
"tokenizer": {
"start_tokens": ["<START>"],
"end_tokens": ["<END>"]
},
"entities_tokenizer": {
"word_splitter": {
"type": "entities_splitter"
}
}
},
"train_data_path":
"./data/nli_datasets/SciTailV1/tsv_format/conceptnet_processed_twohop/entities/scitail_1.0_train.tsv",
"validation_data_path":
"./data/nli_datasets/SciTailV1/tsv_format/conceptnet_processed_twohop/entities/scitail_1.0_dev.tsv",
"model": {
"type": "graph_and_text",
"text_model": {
"type": "match_lstm",
"text_field_embedder": {
"tokens": {
"type": "embedding",
"pretrained_file": "./data/embeddings/glove/glove.840B.300d.txt.gz",
"embedding_dim": 300,
"trainable": false
}
},
"encoder": {
"type": "gru",
"input_size": 300,
"hidden_size": 300,
"num_layers": 1,
"bidirectional": true
},
"inter_attention": { "type": "dot_product" },
"initializer": [
[".*linear_layers.*weight", { "type": "xavier_normal" }],
[
".*token_embedder_tokens._projection.*weight",
{ "type": "xavier_normal" }
]
]
},
"graph_model": {
"type": "simple_graph",
"entities_embedder": {
"tokens": {
"type": "embedding",
"projection_dim": 100,
"pretrained_file": "./data/embeddings/conceptnet/transh.txt.gz",
"embedding_dim": 300,
"trainable": true
}
},
"inter_attention": {
"type": "dot_product"
},
"project_feedforward": {
"input_dim": 400,
"num_layers": 2,
"hidden_dims": 100,
"activations": "relu",
"dropout": 0.2
},
"initializer": [
[".*linear_layers.*weight", { "type": "xavier_normal" }],
[
".*token_embedder_tokens._projection.*weight",
{ "type": "xavier_normal" }
]
]
},
"classify_feed_forward": {
"input_dim": 2800,
"num_layers": 1,
"hidden_dims": 2,
"activations": "linear",
"dropout": 0.0
}
},
"iterator": {
"type": "bucket",
"sorting_keys": [
["premise", "num_tokens"],
["hypothesis", "num_tokens"],
["premise_entities", "num_tokens"],
["hypothesis_entities", "num_tokens"]
],
"batch_size": 40
},
"trainer": {
"num_epochs": 140,
"patience": 20,
"cuda_device": -1,
"grad_clipping": 5.0,
"validation_metric": "+accuracy",
"optimizer": {
"type": "adam",
"lr": 0.001
}
}
}