Upload kin_en_args.yaml

kin_en_args.yaml

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+name: "kin_en_transformer"
+
+data:
+    src: 
+        lang: "kin"
+        level: "bpe"
+        lowercase: False
+        tokenizer_type: "subword-nmt"
+        num_merges: 4000
+        tokenizer_cfg:
+            num_merges: 4000
+            codes: "bpe.codes.4000"
+            pretokenizer: "none"
+
+    trg: 
+        lang: "en"
+        level: "bpe"
+        lowercase: False
+        tokenizer_type: "subword-nmt"
+        num_merges: 4000
+        tokenizer_cfg:
+            num_merges: 4000
+            codes: "bpe.codes.4000"
+            pretokenizer: "none"
+
+    train: "data/train/kin_en_train"
+    dev:   "data/val/kin_en_val"
+    test:  "data/test/kin_en_test"
+    level: "bpe"
+    # lowercase: False
+    max_sent_length: 100
+    # src_vocab: "models/kin_en_tranformer/src_vocab"
+    # trg_vocab: "models/kin_en_tranformer/src_vocab"
+    dataset_type: "tsv"
+
+testing:
+    beam_size: 15
+    beam_alpha: 1.0
+    eval_metrics: ["bleu"]
+    batch_type: sentence
+    sacrebleu_cfg:                      # sacrebleu options
+        remove_whitespace: True     # `remove_whitespace` option in sacrebleu.corpus_chrf() function (defalut: True)
+        tokenize: "none"            # `tokenize` option in sacrebleu.corpus_bleu() function (options include: "none" (use for already tokenized test data), "13a" (default minimal tokenizer), "intl" which mostly does punctuation and unicode, etc) 
+
+training:
+    #load_model: "{ models/{name}_transformer/1.ckpt" # if uncommented, load a pre-trained model from this checkpoint
+    random_seed: 42
+    optimizer: "adam"
+    normalization: "tokens"
+    adam_betas: [0.9, 0.999] 
+    scheduling: "plateau"           # TODO: try switching from plateau to Noam scheduling
+    patience: 5                     # For plateau: decrease learning rate by decrease_factor if validation score has not improved for this many validation rounds.
+    learning_rate_factor: 0.5       # factor for Noam scheduler (used with Transformer)
+    learning_rate_warmup: 1000      # warmup steps for Noam scheduler (used with Transformer)
+    decrease_factor: 0.7
+    loss: "crossentropy"
+    learning_rate: 0.0003
+    learning_rate_min: 0.00000001
+    weight_decay: 0.0
+    label_smoothing: 0.1
+    batch_size: 256
+    batch_type: "token"
+    eval_batch_size: 3600
+    eval_batch_type: "token"
+    batch_multiplier: 1
+    early_stopping_metric: "ppl"
+    epochs: 30                     # TODO: Decrease for when playing around and checking of working. Around 30 is sufficient to check if its working at all
+    validation_freq: 1000          # TODO: Set to at least once per epoch.
+    logging_freq: 100
+    eval_metric: "bleu"
+    model_dir: "models/kin_en_transformer"
+    overwrite: False               # TODO: Set to True if you want to overwrite possibly existing models. 
+    shuffle: True
+    use_cuda: True
+    max_output_length: 100
+    print_valid_sents: [0, 1, 2, 3]
+    keep_last_ckpts: 3
+
+model:
+    initializer: "xavier_normal"
+    bias_initializer: "zeros"
+    init_gain: 1.0
+    embed_initializer: "xavier_normal"
+    embed_init_gain: 1.0
+    tied_embeddings: False
+    tied_softmax: True
+    encoder:
+        type: "transformer"
+        num_layers: 6
+        num_heads: 8
+        embeddings:
+            embedding_dim: 256
+            scale: True
+            dropout: 0.
+        # typically ff_size = 4 x hidden_size
+        hidden_size: 256
+        ff_size: 1024
+        dropout: 0.1
+        layer_norm: "post"
+    decoder:
+        type: "transformer"
+        num_layers: 6
+        num_heads: 8
+        embeddings:
+            embedding_dim: 256
+            scale: True
+            dropout: 0.
+        # typically ff_size = 4 x hidden_size
+        hidden_size: 256
+        ff_size: 1024
+        dropout: 0.1
+        layer_norm: "post"