Commit from model create scripts
Browse files- .gitattributes +2 -8
- config.gin +179 -0
- config.json +39 -0
- flax_model.msgpack +3 -0
- model-info.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- spiece.model +3 -0
- tokenizer.json +3 -0
- tokenizer_config.json +1 -0
- train/events.out.tfevents.1673627650.t1v-n-a0e4cb08-w-0.1714764.0.v2 +3 -0
- training_eval/translate_long/events.out.tfevents.1673627651.t1v-n-a0e4cb08-w-0.1714764.1.v2 +3 -0
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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config.gin
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1 |
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from __gin__ import dynamic_registration
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import __main__ as train_script
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import seqio
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from t5.data import mixtures
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from t5x import adafactor
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from t5x.examples.t5 import network
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from t5x import gin_utils
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from t5x import models
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from t5x import partitioning
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from t5x import trainer
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from t5x import utils
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import tasks
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# Macros:
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# ==============================================================================
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BATCH_SIZE = 128
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DROPOUT_RATE = 0.1
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EVAL_PERIOD = 1000
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EVAL_STEPS = 20
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EVALUATOR_NUM_EXAMPLES = None
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EVALUATOR_USE_MEMORY_CACHE = True
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INITIAL_CHECKPOINT_PATH = \
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'gs://nb-t5x-us-central2/norwegian_NCC_plus_English_pluss200k_balanced_bokmaal_nynorsk_t5x_base/checkpoint_1700000'
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JSON_WRITE_N_RESULTS = None
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LABEL_SMOOTHING = 0.0
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LOSS_NORMALIZING_FACTOR = None
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MIXTURE_OR_TASK_MODULE = None
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MIXTURE_OR_TASK_NAME = 'translate_long'
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MODEL = @models.EncoderDecoderModel()
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MODEL_DIR = 'gs://nb-t5x-us-central2/finetuned/nynorsk_balanced_base_long_v1'
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OPTIMIZER = @adafactor.Adafactor()
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RANDOM_SEED = 0
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TASK_FEATURE_LENGTHS = {'inputs': 512, 'targets': 512}
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TRAIN_STEPS = 1705000
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USE_CACHED_TASKS = False
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USE_HARDWARE_RNG = False
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VOCABULARY = @seqio.SentencePieceVocabulary()
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Z_LOSS = 0.0001
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# Parameters for adafactor.Adafactor:
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# ==============================================================================
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adafactor.Adafactor.decay_rate = 0.8
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adafactor.Adafactor.logical_factor_rules = \
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@adafactor.standard_logical_factor_rules()
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adafactor.Adafactor.step_offset = 0
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# Parameters for utils.CheckpointConfig:
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# ==============================================================================
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utils.CheckpointConfig.restore = @utils.RestoreCheckpointConfig()
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utils.CheckpointConfig.save = @utils.SaveCheckpointConfig()
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# Parameters for utils.create_learning_rate_scheduler:
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# ==============================================================================
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utils.create_learning_rate_scheduler.base_learning_rate = 0.001
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utils.create_learning_rate_scheduler.factors = 'constant'
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utils.create_learning_rate_scheduler.warmup_steps = 1000
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# Parameters for infer_eval/utils.DatasetConfig:
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59 |
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# ==============================================================================
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infer_eval/utils.DatasetConfig.batch_size = %BATCH_SIZE
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infer_eval/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
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infer_eval/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
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63 |
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infer_eval/utils.DatasetConfig.pack = False
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64 |
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infer_eval/utils.DatasetConfig.seed = 42
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65 |
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infer_eval/utils.DatasetConfig.shuffle = False
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66 |
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infer_eval/utils.DatasetConfig.split = 'validation'
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infer_eval/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
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68 |
+
infer_eval/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
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69 |
+
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70 |
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# Parameters for train/utils.DatasetConfig:
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71 |
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# ==============================================================================
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72 |
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train/utils.DatasetConfig.batch_size = %BATCH_SIZE
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73 |
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train/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
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74 |
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train/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
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75 |
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train/utils.DatasetConfig.pack = True
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76 |
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train/utils.DatasetConfig.seed = None
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77 |
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train/utils.DatasetConfig.shuffle = True
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78 |
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train/utils.DatasetConfig.split = 'train'
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79 |
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train/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
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80 |
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train/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
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81 |
+
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82 |
+
# Parameters for train_eval/utils.DatasetConfig:
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83 |
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# ==============================================================================
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84 |
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train_eval/utils.DatasetConfig.batch_size = %BATCH_SIZE
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85 |
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train_eval/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
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86 |
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train_eval/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
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87 |
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train_eval/utils.DatasetConfig.pack = True
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88 |
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train_eval/utils.DatasetConfig.seed = 42
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89 |
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train_eval/utils.DatasetConfig.shuffle = False
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90 |
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train_eval/utils.DatasetConfig.split = 'validation'
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91 |
+
train_eval/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
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92 |
+
train_eval/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
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93 |
+
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94 |
+
# Parameters for models.EncoderDecoderModel:
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95 |
+
# ==============================================================================
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96 |
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models.EncoderDecoderModel.input_vocabulary = %VOCABULARY
|
97 |
+
models.EncoderDecoderModel.label_smoothing = %LABEL_SMOOTHING
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98 |
+
models.EncoderDecoderModel.loss_normalizing_factor = %LOSS_NORMALIZING_FACTOR
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99 |
+
models.EncoderDecoderModel.module = @network.Transformer()
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100 |
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models.EncoderDecoderModel.optimizer_def = %OPTIMIZER
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models.EncoderDecoderModel.output_vocabulary = %VOCABULARY
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models.EncoderDecoderModel.z_loss = %Z_LOSS
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103 |
+
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104 |
+
# Parameters for seqio.Evaluator:
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105 |
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# ==============================================================================
|
106 |
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seqio.Evaluator.logger_cls = \
|
107 |
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[@seqio.PyLoggingLogger, @seqio.TensorBoardLogger, @seqio.JSONLogger]
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108 |
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seqio.Evaluator.num_examples = %EVALUATOR_NUM_EXAMPLES
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109 |
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seqio.Evaluator.use_memory_cache = %EVALUATOR_USE_MEMORY_CACHE
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110 |
+
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111 |
+
# Parameters for seqio.JSONLogger:
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112 |
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# ==============================================================================
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113 |
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seqio.JSONLogger.write_n_results = %JSON_WRITE_N_RESULTS
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114 |
+
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115 |
+
# Parameters for partitioning.PjitPartitioner:
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116 |
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# ==============================================================================
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117 |
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partitioning.PjitPartitioner.logical_axis_rules = \
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118 |
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@partitioning.standard_logical_axis_rules()
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119 |
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partitioning.PjitPartitioner.model_parallel_submesh = None
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120 |
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partitioning.PjitPartitioner.num_partitions = 1
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121 |
+
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122 |
+
# Parameters for utils.RestoreCheckpointConfig:
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123 |
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# ==============================================================================
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124 |
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utils.RestoreCheckpointConfig.dtype = 'float32'
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125 |
+
utils.RestoreCheckpointConfig.mode = 'specific'
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126 |
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utils.RestoreCheckpointConfig.path = %INITIAL_CHECKPOINT_PATH
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127 |
+
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128 |
+
# Parameters for utils.SaveCheckpointConfig:
|
129 |
+
# ==============================================================================
|
130 |
+
utils.SaveCheckpointConfig.dtype = 'float32'
|
131 |
+
utils.SaveCheckpointConfig.keep = None
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132 |
+
utils.SaveCheckpointConfig.period = 1000
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133 |
+
utils.SaveCheckpointConfig.save_dataset = False
|
134 |
+
|
135 |
+
# Parameters for seqio.SentencePieceVocabulary:
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136 |
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# ==============================================================================
|
137 |
+
seqio.SentencePieceVocabulary.sentencepiece_model_file = \
|
138 |
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'gs://t5-data/vocabs/mc4.250000.100extra/sentencepiece.model'
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139 |
+
|
140 |
+
# Parameters for network.T5Config:
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141 |
+
# ==============================================================================
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142 |
+
network.T5Config.dropout_rate = %DROPOUT_RATE
|
143 |
+
network.T5Config.dtype = 'bfloat16'
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144 |
+
network.T5Config.emb_dim = 768
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145 |
+
network.T5Config.head_dim = 64
|
146 |
+
network.T5Config.logits_via_embedding = False
|
147 |
+
network.T5Config.mlp_activations = ('gelu', 'linear')
|
148 |
+
network.T5Config.mlp_dim = 2048
|
149 |
+
network.T5Config.num_decoder_layers = 12
|
150 |
+
network.T5Config.num_encoder_layers = 12
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151 |
+
network.T5Config.num_heads = 12
|
152 |
+
network.T5Config.vocab_size = 250112
|
153 |
+
|
154 |
+
# Parameters for train_script.train:
|
155 |
+
# ==============================================================================
|
156 |
+
train_script.train.checkpoint_cfg = @utils.CheckpointConfig()
|
157 |
+
train_script.train.eval_period = %EVAL_PERIOD
|
158 |
+
train_script.train.eval_steps = %EVAL_STEPS
|
159 |
+
train_script.train.infer_eval_dataset_cfg = @infer_eval/utils.DatasetConfig()
|
160 |
+
train_script.train.inference_evaluator_cls = @seqio.Evaluator
|
161 |
+
train_script.train.model = %MODEL
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162 |
+
train_script.train.model_dir = %MODEL_DIR
|
163 |
+
train_script.train.partitioner = @partitioning.PjitPartitioner()
|
164 |
+
train_script.train.random_seed = %RANDOM_SEED
|
165 |
+
train_script.train.summarize_config_fn = @gin_utils.summarize_gin_config
|
166 |
+
train_script.train.total_steps = %TRAIN_STEPS
|
167 |
+
train_script.train.train_dataset_cfg = @train/utils.DatasetConfig()
|
168 |
+
train_script.train.train_eval_dataset_cfg = @train_eval/utils.DatasetConfig()
|
169 |
+
train_script.train.trainer_cls = @trainer.Trainer
|
170 |
+
train_script.train.use_hardware_rng = %USE_HARDWARE_RNG
|
171 |
+
|
172 |
+
# Parameters for trainer.Trainer:
|
173 |
+
# ==============================================================================
|
174 |
+
trainer.Trainer.learning_rate_fn = @utils.create_learning_rate_scheduler()
|
175 |
+
trainer.Trainer.num_microbatches = None
|
176 |
+
|
177 |
+
# Parameters for network.Transformer:
|
178 |
+
# ==============================================================================
|
179 |
+
network.Transformer.config = @network.T5Config()
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config.json
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{
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"_name_or_path": "/home/perk/models/nynorsk_North_base_long",
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"architectures": [
|
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"T5ForConditionalGeneration"
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],
|
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"d_ff": 2048,
|
7 |
+
"d_kv": 64,
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+
"d_model": 768,
|
9 |
+
"decoder_start_token_id": 0,
|
10 |
+
"dropout_rate": 0.1,
|
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+
"eos_token_id": 1,
|
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+
"feed_forward_proj": "gated-gelu",
|
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+
"initializer_factor": 1.0,
|
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"is_encoder_decoder": true,
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+
"layer_norm_epsilon": 1e-06,
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"max_length": 512,
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"model_type": "t5",
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"num_decoder_layers": 12,
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"num_heads": 12,
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+
"num_layers": 12,
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"output_past": true,
|
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+
"pad_token_id": 0,
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+
"relative_attention_max_distance": 128,
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+
"relative_attention_num_buckets": 32,
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+
"task_specific_params": {
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+
"text-generation": {
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"max_length": 512
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},
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"translation": {
|
30 |
+
"max_length": 512
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}
|
32 |
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