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Commit 400ce5a0 authored by Caroline DE POURTALES's avatar Caroline DE POURTALES
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change supertagger

parent 304c6295
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2 merge requests!6Linker with transformer,!5Linker with transformer
......@@ -12,16 +12,14 @@ max_atoms_in_one_type=250
dim_encoder = 768
[MODEL_DECODER]
dim_decoder = 32
nhead=4
dropout=0.1
teacher_forcing=0.05
dim_feedforward=512
layer_norm_eps=1e-5
[MODEL_LINKER]
nhead=4
dim_feedforward=246
dim_embedding_atoms=32
dim_polarity_transfo=128
layer_norm_eps=1e-5
dim_embedding_atoms=256
dim_polarity_transfo=256
dropout=0.1
sinkhorn_iters=3
......
......@@ -43,7 +43,7 @@ class Linker(Module):
self.dim_polarity_transfo = int(Configuration.modelLinkerConfig['dim_polarity_transfo'])
self.dim_embedding_atoms = int(Configuration.modelLinkerConfig['dim_embedding_atoms'])
self.sinkhorn_iters = int(Configuration.modelLinkerConfig['sinkhorn_iters'])
self.nhead = int(Configuration.modelLinkerConfig['nhead'])
self.nhead = int(Configuration.modelDecoderConfig['nhead'])
self.max_len_sentence = int(Configuration.datasetConfig['max_len_sentence'])
self.max_atoms_in_sentence = int(Configuration.datasetConfig['max_atoms_in_sentence'])
self.max_atoms_in_one_type = int(Configuration.datasetConfig['max_atoms_in_one_type'])
......@@ -77,7 +77,7 @@ class Linker(Module):
lr=learning_rate)
self.scheduler = get_cosine_schedule_with_warmup(self.optimizer,
num_warmup_steps=0,
num_training_steps=100)
num_training_steps=float(Configuration.modelTrainingConfig['epoch']))
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
......
......@@ -33,11 +33,11 @@ class AttentionDecoderLayer(Module):
# init params
dim_encoder = int(Configuration.modelEncoderConfig['dim_encoder'])
dim_decoder = int(Configuration.modelDecoderConfig['dim_decoder'])
nhead = int(Configuration.modelLinkerConfig['nhead'])
dropout = float(Configuration.modelLinkerConfig['dropout'])
dim_feedforward = int(Configuration.modelLinkerConfig['dim_feedforward'])
layer_norm_eps = float(Configuration.modelLinkerConfig['layer_norm_eps'])
dim_decoder = int(Configuration.modelLinkerConfig['dim_embedding_atoms'])
nhead = int(Configuration.modelDecoderConfig['nhead'])
dropout = float(Configuration.modelDecoderConfig['dropout'])
dim_feedforward = int(Configuration.modelDecoderConfig['dim_feedforward'])
layer_norm_eps = float(Configuration.modelDecoderConfig['layer_norm_eps'])
# layers
self.dropout = Dropout(dropout)
......
......@@ -10,13 +10,13 @@ from utils import pad_sequence
class FFN(Module):
"Implements FFN equation."
def __init__(self, d_model, d_ff, dropout=0.1):
def __init__(self, d_model, d_ff, dropout=0.1, d_out=None):
super(FFN, self).__init__()
self.ffn = Sequential(
Linear(d_model, d_ff, bias=False),
GELU(),
Dropout(dropout),
Linear(d_ff, d_model, bias=False)
Linear(d_ff, d_out if d_out is not None else d_model, bias=False)
)
def forward(self, x):
......
......@@ -13,8 +13,6 @@ epochs = int(Configuration.modelTrainingConfig['epoch'])
file_path_axiom_links = 'Datasets/gold_dataset_links.csv'
df_axiom_links = read_csv_pgbar(file_path_axiom_links, nb_sentences)
sentences_batch = df_axiom_links["Sentences"].tolist()
print("Linker")
linker = Linker("models/model_supertagger.pt")
print("Linker Training")
......
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