quickmt-fr-en / eole-config.yaml
radinplaid's picture
Upload folder using huggingface_hub
d00d112 verified
raw
history blame
2.2 kB
## IO
save_data: fr-en/data_spm
overwrite: True
seed: 1234
report_every: 100
valid_metrics: ["BLEU"]
tensorboard: true
tensorboard_log_dir: tensorboard
### Vocab
src_vocab: fr-en/joint.eole.vocab
tgt_vocab: fr-en/joint.eole.vocab
src_vocab_size: 50000
tgt_vocab_size: 50000
vocab_size_multiple: 8
share_vocab: True
n_sample: 0
data:
corpus_1:
path_src: hf://quickmt/quickmt-train.fr-en/fr
path_tgt: hf://quickmt/quickmt-train.fr-en/en
path_sco: hf://quickmt/quickmt-train.fr-en/sco
valid:
path_src: fr-en/dev.src
path_tgt: fr-en/dev.tgt
transforms: [sentencepiece, filtertoolong]
transforms_configs:
sentencepiece:
src_subword_model: "fr-en/joint.spm.model"
tgt_subword_model: "fr-en/joint.spm.model"
filtertoolong:
src_seq_length: 256
tgt_seq_length: 256
training:
# Run configuration
model_path: fr-en/model
keep_checkpoint: 4
save_checkpoint_steps: 2000
train_steps: 100000
valid_steps: 2000
# Train on a single GPU
world_size: 1
gpu_ranks: [0]
# Batching
batch_type: "tokens"
batch_size: 8192
valid_batch_size: 8192
batch_size_multiple: 8
accum_count: [16]
accum_steps: [0]
# Optimizer & Compute
compute_dtype: "bf16"
optim: "pagedadamw8bit"
#optim: "adamw"
learning_rate: 2.0
warmup_steps: 10000
decay_method: "noam"
adam_beta2: 0.998
# Data loading
bucket_size: 128000
num_workers: 4
prefetch_factor: 100
# Hyperparams
dropout_steps: [0]
dropout: [0.1]
attention_dropout: [0.1]
max_grad_norm: 2
label_smoothing: 0.1
average_decay: 0.0001
param_init_method: xavier_uniform
normalization: "tokens"
model:
architecture: "transformer"
layer_norm: standard
share_embeddings: true
share_decoder_embeddings: true
add_ffnbias: true
mlp_activation_fn: gelu
add_estimator: false
add_qkvbias: false
norm_eps: 1e-6
hidden_size: 1024
encoder:
layers: 8
decoder:
layers: 2
heads: 8
transformer_ff: 4096
embeddings:
word_vec_size: 1024
position_encoding_type: "SinusoidalInterleaved"