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bert_baseline_ft_silver.jsonnet
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bert_baseline_ft_silver.jsonnet
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local transformer_model_name = std.extVar("EMBEDDING_MODEL_NAME");
local embedding_dim = std.parseInt(std.extVar("EMBEDDING_DIMS")) + 64 * 2 + 300;
local corpus_name = std.extVar("CORPUS");
local context_hidden_size = 400;
local encoder_hidden_dim = 512;
local context_encoder = {
"type": "lstm",
"input_size": embedding_dim,
"hidden_size": context_hidden_size / 4, // 4 <= 2 bilstms applied to 2 sentences
"num_layers": 1,
"bidirectional": true,
"dropout": 0.2
};
local token_features = {
"pos_tags": {"source_key": "pos", "label_namespace": "upos"},
"cpos_tags": {"source_key": "cpos", "label_namespace": "xpos"},
"deprel_tags": {"source_key": "deprel", "label_namespace": "deprel"},
"dep_chunk_tags": {"source_key": "depchunk", "label_namespace": "depchunk"},
"parent_clauses": {"source_key": "parentclauses", "label_namespace": "parent_clauses"},
"s_type": {"source_key": "s_type", "label_namespace": "s_type"},
"case_tags": {"source_key": "case", "label_namespace": "case"},
"genre_tags": {"source_key": "genre", "label_namespace": "genre"},
"head_distances": {"source_key": "head_dist", "xform_fn": "abs_natural_log"},
"document_depth": {"source_key": "sent_doc_percentile"},
"sentence_length": {"source_key": "s_len", "xform_fn": "natural_log"},
"token_lengths": {"source_key": "tok_len", "xform_fn": "natural_log"}
};
// For small corpora, make this number reflect the size of train
// For larger corpora, use a smaller number, aiming for 1/3 of total size
local batches_per_epoch = {
"deu.rst.pcc": 541,
"eng.pdtb.pdtb": 3000, // real: 10980
"eng.rst.gum": 1700, // real: 3475
"eng.rst.rstdt": 2000, // real: 4001
"eng.sdrt.stac": 1200, // real: 2395
"eus.rst.ert": 634,
"fas.rst.prstc": 1025,
"fra.sdrt.annodis": 547,
"nld.rst.nldt": 402,
"por.rst.cstn": 1037,
"rus.rst.rrt": 2500, // real: 7217
"spa.rst.rststb": 560,
"spa.rst.sctb": 110,
"tur.pdtb.tdb": 613,
"zho.pdtb.cdtb": 915,
"zho.rst.sctb": 110
};
// For more info on config files generally, see https://guide.allennlp.org/using-config-files
{
"dataset_reader" : {
"type": "disrpt_2021_seg",
"token_indexers": {
"tokens": {
"type": "pretrained_transformer_mismatched",
"model_name": transformer_model_name,
"max_length": 512
},
"fasttext": {
"type": "single_id",
"namespace": "fasttext",
},
"token_characters": import "../../components/char_indexer.libsonnet"
},
"tokenizer": {
"type": "whitespace"
},
"token_features": token_features
},
"model": {
"type": "disrpt_2021_seg_baseline",
"embedder": {
"token_embedders": {
"tokens": {
"type": "pretrained_transformer_mismatched",
"model_name": transformer_model_name,
"train_parameters": true,
"last_layer_only": true,
"max_length": 512
},
"fasttext": {
"type": "embedding",
"vocab_namespace": "fasttext",
"embedding_dim": 300,
"trainable": false,
"pretrained_file": std.extVar("FASTTEXT_EMBEDDING_FILE")
},
"token_characters": import "../../components/char_embedder.libsonnet"
}
},
// seq2vec encoders for neighbor sentences
"prev_sentence_encoder": context_encoder,
"next_sentence_encoder": context_encoder,
// our encoder isn't fully configurable here because its input size needs to be determined
// at the start of the program. so, it'll always be a bilstm, but you can use the two items
// below to configure the most important hyperparameters it has
"encoder_hidden_dim": encoder_hidden_dim,
"encoder_recurrent_dropout": 0.1,
// end encoder hyperparams
"dropout": 0.4,
"feature_dropout": 0.0,
"token_features": token_features,
"use_crf": if std.extVar("USE_CRF") == "1" then true else false
},
"train_data_path": std.extVar("TRAIN_DATA_PATH"),
"validation_data_path": std.extVar("VALIDATION_DATA_PATH"),
"data_loader": {
"batches_per_epoch": batches_per_epoch[corpus_name],
"batch_size": 4,
"shuffle": true
},
"trainer": {
"optimizer": {
"type": "huggingface_adamw",
"lr": 5e-4,
"parameter_groups": [
[[".*transformer.*"], {"lr": 1e-5}]
]
},
"patience": 10,
"num_epochs": 60,
// probably best to just use loss
"validation_metric": "+span_f1"
}
}