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yhavinga
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t5_1_1-base-nl36-dutch

Text2Text Generation
Transformers
PyTorch
JAX
TensorBoard
t5
Inference Endpoints
text-generation-inference
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yhavinga commited on
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Add model

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Files changed (18) hide show
  1. config.gin +149 -0
  2. config.json +31 -0
  3. flax_model.msgpack +3 -0
  4. model-info.txt +0 -0
  5. pytorch_model.bin +3 -0
  6. special_tokens_map.json +107 -0
  7. spiece.model +3 -0
  8. spiece.vocab +0 -0
  9. tokenizer_config.json +113 -0
  10. train/events.out.tfevents.1670764801.t1v-n-82e561a1-w-0.1168530.0.v2 +3 -0
  11. train/events.out.tfevents.1671135229.t1v-n-82e561a1-w-0.1383256.0.v2 +3 -0
  12. train/events.out.tfevents.1671186857.t1v-n-7fd9879f-w-0.50950.0.v2 +3 -0
  13. training_eval/mc4_nedd_wiki_news_mix_1/events.out.tfevents.1670764801.t1v-n-82e561a1-w-0.1168530.2.v2 +3 -0
  14. training_eval/mc4_nedd_wiki_news_mix_1/events.out.tfevents.1671135229.t1v-n-82e561a1-w-0.1383256.2.v2 +3 -0
  15. training_eval/mc4_nedd_wiki_news_mix_1/events.out.tfevents.1671186857.t1v-n-7fd9879f-w-0.50950.2.v2 +3 -0
  16. training_eval/mc4_nl_span_corruption/events.out.tfevents.1670764801.t1v-n-82e561a1-w-0.1168530.1.v2 +3 -0
  17. training_eval/mc4_nl_span_corruption/events.out.tfevents.1671135229.t1v-n-82e561a1-w-0.1383256.1.v2 +3 -0
  18. training_eval/mc4_nl_span_corruption/events.out.tfevents.1671186857.t1v-n-7fd9879f-w-0.50950.1.v2 +3 -0
config.gin ADDED
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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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+ import t5.data.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.nedd_tasks
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+
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+ # Macros:
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+ # ==============================================================================
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+ BATCH_SIZE = 128
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+ DROPOUT_RATE = 0.0
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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 = 'mc4_nedd_wiki_news_mix_1'
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+ MODEL = @models.EncoderDecoderModel()
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+ MODEL_DIR = 't5_1_1_base_nl36_mc4_nedd_wiki_news_nl'
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+ OPTIMIZER = @adafactor.Adafactor()
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+ RANDOM_SEED = None
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+ SHUFFLE_TRAIN_EXAMPLES = True
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+ TASK_FEATURE_LENGTHS = {'inputs': 512, 'targets': 114}
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+ TRAIN_STEPS = 1000000
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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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+
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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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+
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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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+
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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 = 1.0
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+ utils.create_learning_rate_scheduler.factors = 'constant * rsqrt_decay'
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+ utils.create_learning_rate_scheduler.warmup_steps = 10000
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+
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+ # Parameters for train/utils.DatasetConfig:
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+ # ==============================================================================
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+ train/utils.DatasetConfig.batch_size = %BATCH_SIZE
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+ train/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
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+ train/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
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+ train/utils.DatasetConfig.pack = True
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+ train/utils.DatasetConfig.seed = None
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+ train/utils.DatasetConfig.shuffle = %SHUFFLE_TRAIN_EXAMPLES
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+ train/utils.DatasetConfig.split = 'train'
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+ train/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
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+ train/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
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+
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+ # Parameters for train_eval/utils.DatasetConfig:
65
+ # ==============================================================================
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+ train_eval/utils.DatasetConfig.batch_size = %BATCH_SIZE
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+ train_eval/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
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+ train_eval/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
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+ train_eval/utils.DatasetConfig.pack = True
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+ train_eval/utils.DatasetConfig.seed = 42
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+ train_eval/utils.DatasetConfig.shuffle = False
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+ train_eval/utils.DatasetConfig.split = 'validation'
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+ train_eval/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
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+ train_eval/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
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+
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+ # Parameters for models.EncoderDecoderModel:
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+ # ==============================================================================
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+ models.EncoderDecoderModel.input_vocabulary = %VOCABULARY
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+ models.EncoderDecoderModel.label_smoothing = %LABEL_SMOOTHING
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+ models.EncoderDecoderModel.loss_normalizing_factor = %LOSS_NORMALIZING_FACTOR
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+ models.EncoderDecoderModel.module = @network.Transformer()
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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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+
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+ # Parameters for partitioning.PjitPartitioner:
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+ # ==============================================================================
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+ partitioning.PjitPartitioner.logical_axis_rules = \
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+ @partitioning.standard_logical_axis_rules()
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+ partitioning.PjitPartitioner.model_parallel_submesh = None
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+ partitioning.PjitPartitioner.num_partitions = 1
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+
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+ # Parameters for utils.RestoreCheckpointConfig:
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+ # ==============================================================================
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+ utils.RestoreCheckpointConfig.path = []
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+
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+ # Parameters for utils.SaveCheckpointConfig:
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+ # ==============================================================================
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+ utils.SaveCheckpointConfig.dtype = 'float32'
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+ utils.SaveCheckpointConfig.keep = 4
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+ utils.SaveCheckpointConfig.period = 50000
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+ utils.SaveCheckpointConfig.save_dataset = False
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+ utils.SaveCheckpointConfig.use_gda = False
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+
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+ # Parameters for seqio.SentencePieceVocabulary:
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+ # ==============================================================================
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+ seqio.SentencePieceVocabulary.sentencepiece_model_file = \
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+ 'gs://t5-dutch-english/vocabs/nedd.32000.100extra/spiece.model'
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+
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+ # Parameters for network.T5Config:
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+ # ==============================================================================
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+ network.T5Config.dropout_rate = %DROPOUT_RATE
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+ network.T5Config.dtype = 'bfloat16'
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+ network.T5Config.emb_dim = 768
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+ network.T5Config.head_dim = 64
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+ network.T5Config.logits_via_embedding = False
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+ network.T5Config.mlp_activations = ('gelu', 'linear')
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+ network.T5Config.mlp_dim = 3072
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+ network.T5Config.num_decoder_layers = 36
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+ network.T5Config.num_encoder_layers = 36
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+ network.T5Config.num_heads = 12
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+ network.T5Config.vocab_size = 32128
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+
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+ # Parameters for train_script.train:
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+ # ==============================================================================
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+ train_script.train.checkpoint_cfg = @utils.CheckpointConfig()
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+ train_script.train.eval_period = 2000
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+ train_script.train.eval_steps = 20
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+ train_script.train.infer_eval_dataset_cfg = None
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+ train_script.train.model = %MODEL
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+ train_script.train.model_dir = %MODEL_DIR
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+ train_script.train.partitioner = @partitioning.PjitPartitioner()
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+ train_script.train.random_seed = %RANDOM_SEED
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+ train_script.train.stats_period = 100
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+ train_script.train.summarize_config_fn = @gin_utils.summarize_gin_config
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+ train_script.train.total_steps = %TRAIN_STEPS
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+ train_script.train.train_dataset_cfg = @train/utils.DatasetConfig()
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+ train_script.train.train_eval_dataset_cfg = @train_eval/utils.DatasetConfig()
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+ train_script.train.trainer_cls = @trainer.Trainer
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+ train_script.train.use_hardware_rng = %USE_HARDWARE_RNG
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+
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+ # Parameters for trainer.Trainer:
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+ # ==============================================================================
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+ trainer.Trainer.learning_rate_fn = @utils.create_learning_rate_scheduler()
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+ trainer.Trainer.num_microbatches = None
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+
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+ # Parameters for network.Transformer:
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+ # ==============================================================================
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+ network.Transformer.config = @network.T5Config()
config.json ADDED
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+ "_name_or_path": "hf/t5_1_1-base-nl36-dutch",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ "d_ff": 3072,
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+ "decoder_start_token_id": 0,
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+ "dense_act_fn": "gelu_new",
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+ "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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+ "is_gated_act": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "num_decoder_layers": 36,
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+ "num_heads": 12,
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+ "num_layers": 36,
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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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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.24.0",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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