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1) adapt a number of codes in several classes as a consequence of Tag…
…gingTokenClusteror implementation in AcknowledgmentParser class; 2) add new feature vectors for improving acknowledgement model; 3) automatically split data (80/20)-train-evaluate a new acknowledgment model; 4) add new n-fold evaluation results. Old results are still kept for comparison purposes.
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Original file line number | Diff line number | Diff line change |
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@@ -1,22 +1,19 @@ | ||
{ | ||
"model_name": "acknowledgment", | ||
"model_type": "BidLSTM_CRF", | ||
"embeddings_name": "glove-840B", | ||
"char_vocab_size": 111, | ||
"case_vocab_size": 8, | ||
"char_embedding_size": 25, | ||
"num_char_lstm_units": 25, | ||
"max_char_length": 30, | ||
"max_sequence_length": null, | ||
"word_embedding_size": 300, | ||
"num_word_lstm_units": 100, | ||
"case_embedding_size": 5, | ||
"dropout": 0.5, | ||
"recurrent_dropout": 0.25, | ||
"use_char_feature": true, | ||
"use_crf": true, | ||
"fold_number": 1, | ||
"batch_size": 20, | ||
"use_ELMo": false, | ||
"use_BERT": false | ||
"case_vocab_size": 8, | ||
"model_name": "acknowledgment", | ||
"num_char_lstm_units": 25, | ||
"num_word_lstm_units": 100, | ||
"case_embedding_size": 5, | ||
"use_crf": true, | ||
"fold_number": 1, | ||
"char_vocab_size": 206, | ||
"dropout": 0.5, | ||
"model_type": "BidLSTM_CRF", | ||
"batch_size": 20, | ||
"recurrent_dropout": 0.25, | ||
"max_char_length": 30, | ||
"char_embedding_size": 25, | ||
"use_char_feature": true, | ||
"embeddings_name": "glove-840B", | ||
"word_embedding_size": 300 | ||
} |
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