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# change to list chars of your dataset or use default vietnamese chars
vocab: 'aAร รแบฃแบขรฃรรกรแบกแบ ฤฤแบฑแบฐแบณแบฒแบตแบดแบฏแบฎแบทแบถรขรแบงแบฆแบฉแบจแบซแบชแบฅแบคแบญแบฌbBcCdDฤฤeEรจรแบปแบบแบฝแบผรฉรแบนแบธรชรแปแปแปแปแป
แปแบฟแบพแปแปfFgGhHiIรฌรแปแปฤฉฤจรญรแปแปjJkKlLmMnNoOรฒรแปแปรตรรณรแปแปรดรแปแปแปแปแปแปแปแปแปแปฦกฦ แปแปแปแปแปกแป แปแปแปฃแปขpPqQrRsStTuUรนรแปงแปฆลฉลจรบรแปฅแปคฦฐฦฏแปซแปชแปญแปฌแปฏแปฎแปฉแปจแปฑแปฐvVwWxXyYแปณแปฒแปทแปถแปนแปธรฝรแปตแปดzZ0123456789!"#$%&''()*+,-./:;<=>?@[\]^_`{|}~ '
# cpu, cuda, cuda:0
device: cuda:0
seq_modeling: transformer
transformer:
d_model: 256
nhead: 8
num_encoder_layers: 6
num_decoder_layers: 6
dim_feedforward: 2048
max_seq_length: 1024
pos_dropout: 0.1
trans_dropout: 0.1
optimizer:
max_lr: 0.0003
pct_start: 0.1
trainer:
batch_size: 32
print_every: 200
valid_every: 4000
iters: 100000
# where to save our model for prediction
export: ./weights/transformerocr.pth
checkpoint: ./checkpoint/transformerocr_checkpoint.pth
log: ./train.log
# null to disable compuate accuracy, or change to number of sample to enable validiation while training
metrics: null
dataset:
# name of your dataset
name: data
# path to annotation and image
data_root: ./img/
train_annotation: annotation_train.txt
valid_annotation: annotation_val_small.txt
# resize image to 32 height, larger height will increase accuracy
image_height: 32
image_min_width: 32
image_max_width: 512
dataloader:
num_workers: 3
pin_memory: True
aug:
image_aug: true
masked_language_model: true
predictor:
# disable or enable beamsearch while prediction, use beamsearch will be slower
beamsearch: False
quiet: False
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