/
intents_dstc2.json
116 lines (116 loc) · 2.65 KB
/
intents_dstc2.json
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{
"dataset_reader": {
"name": "dstc2_reader",
"data_path": "dstc2"
},
"dataset_iterator": {
"name": "dstc2_intents_iterator",
"seed": 42
},
"chainer": {
"in": [
"x"
],
"in_y": [
"y"
],
"pipe": [
{
"id": "classes_vocab",
"name": "default_vocab",
"fit_on": [
"y"
],
"level": "token",
"save_path": "vocabs/dstc2_classes.dict",
"load_path": "vocabs/dstc2_classes.dict"
},
{
"in": "x",
"out": "x_tok",
"id": "my_tokenizer",
"name": "nltk_tokenizer",
"tokenizer": "wordpunct_tokenize"
},
{
"in": "x_tok",
"out": "x_emb",
"id": "my_embedder",
"name": "fasttext",
"save_path": "embeddings/dstc2_fastText_model.bin",
"load_path": "embeddings/dstc2_fastText_model.bin",
"dim": 100
},
{
"in": [
"x_emb"
],
"in_y": [
"y"
],
"out": [
"y_labels",
"y_probas_dict"
],
"main": true,
"name": "keras_classification_model",
"save_path": "classifiers/intents_dstc2_v4",
"load_path": "classifiers/intents_dstc2_v4",
"embedding_size": "#my_embedder.dim",
"classes": "#classes_vocab.keys()",
"kernel_sizes_cnn": [
1,
2,
3
],
"filters_cnn": 512,
"confident_threshold": 0.5,
"optimizer": "Adam",
"lear_rate": 0.1,
"lear_rate_decay": 0.1,
"loss": "binary_crossentropy",
"text_size": 15,
"coef_reg_cnn": 1e-4,
"coef_reg_den": 1e-4,
"dropout_rate": 0.5,
"dense_size": 100,
"model_name": "cnn_model"
}
],
"out": [
"y_labels",
"y_probas_dict"
]
},
"train": {
"epochs": 1000,
"batch_size": 64,
"metrics": [
"classification_accuracy",
"classification_f1",
"classification_roc_auc"
],
"validation_patience": 5,
"val_every_n_epochs": 5,
"log_every_n_batches": 100,
"show_examples": false
},
"metadata": {
"requirements": [
"../dp_requirements/tf.txt",
"../dp_requirements/fasttext.txt"
],
"labels": {
"telegram_utils": "IntentModel",
"server_utils": "KerasIntentModel"
},
"download": [
"http://files.deeppavlov.ai/deeppavlov_data/classifiers.tar.gz",
"http://files.deeppavlov.ai/deeppavlov_data/vocabs.tar.gz",
{
"url": "http://files.deeppavlov.ai/deeppavlov_data/embeddings/dstc2_fastText_model.bin",
"subdir": "embeddings"
}
]
}
}