load model from drive and convert
Browse files- .gitignore +1 -0
- README.md +51 -0
- config.json +36 -0
- pytorch_model.bin +3 -0
- t5-v1_1-large-jflAUGv5-training2-ft1-jflAUG-v5_training_metadata.json +1 -0
- trainer_state.json +1972 -0
- training_args.bin +3 -0
.gitignore
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checkpoint-*/
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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model-index:
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- name: t5-v1_1-large-jflAUGv5-training2-ft1-jflAUG-v5
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# t5-v1_1-large-jflAUGv5-training2-ft1-jflAUG-v5
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This model is a fine-tuned version of [pszemraj/t5-v1_1-large-jflAUGv5-training2](https://huggingface.co/pszemraj/t5-v1_1-large-jflAUGv5-training2) on an unknown dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 8e-05
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- train_batch_size: 4
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- eval_batch_size: 1
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- seed: 42
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- distributed_type: multi-GPU
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- gradient_accumulation_steps: 32
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.02
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- num_epochs: 1
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.11.0+cu113
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- Datasets 2.3.2
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- Tokenizers 0.12.1
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config.json
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{
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"_name_or_path": "pszemraj/t5-v1_1-large-jflAUGv5-training2",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 2816,
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"d_kv": 64,
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"d_model": 1024,
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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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"early_stopping": true,
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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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"max_length": 512,
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"min_length": 8,
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"model_type": "t5",
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"no_repeat_ngram_size": 4,
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"num_beams": 2,
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"num_decoder_layers": 24,
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"num_heads": 16,
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"num_layers": 24,
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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": "4.20.1",
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"use_cache": false,
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"vocab_size": 32128
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:aabac79b9c8af99f232493b3dafe804629a8796808d821a05db4da3192fe5e5e
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size 3132672427
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t5-v1_1-large-jflAUGv5-training2-ft1-jflAUG-v5_training_metadata.json
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{"output_dir": "/content/drive/MyDrive/Programming/hf-trainer/t5-v1_1-large-jflAUGv5-training2-ft1-jflAUG-v5", "overwrite_output_dir": true, "do_train": false, "do_eval": false, "do_predict": false, "evaluation_strategy": "no", "prediction_loss_only": false, "per_device_train_batch_size": 4, "per_device_eval_batch_size": 1, "per_gpu_train_batch_size": "None", "per_gpu_eval_batch_size": "None", "gradient_accumulation_steps": 32, "eval_accumulation_steps": "None", "eval_delay": 0, "learning_rate": 8e-05, "weight_decay": 0.08, "adam_beta1": 0.9, "adam_beta2": 0.999, "adam_epsilon": 1e-08, "max_grad_norm": 0.6, "num_train_epochs": 1, "max_steps": -1, "lr_scheduler_type": "cosine", "warmup_ratio": 0.02, "warmup_steps": 0, "log_level": -1, "log_level_replica": -1, "log_on_each_node": true, "logging_dir": "/content/drive/MyDrive/Programming/hf-trainer/t5-v1_1-large-jflAUGv5-training2-ft1-jflAUG-v5/logs", "logging_strategy": "steps", "logging_first_step": false, "logging_steps": 2, "logging_nan_inf_filter": true, "save_strategy": "steps", "save_steps": 25, "save_total_limit": 1, "save_on_each_node": false, "no_cuda": false, "seed": 42, "data_seed": "None", "jit_mode_eval": false, "use_ipex": false, "bf16": false, "fp16": true, "fp16_opt_level": "O1", "half_precision_backend": "cuda_amp", "bf16_full_eval": false, "fp16_full_eval": false, "tf32": "None", "local_rank": 0, "xpu_backend": "None", "tpu_num_cores": "None", "tpu_metrics_debug": false, "debug": "[]", "dataloader_drop_last": false, "eval_steps": "None", "dataloader_num_workers": 0, "past_index": -1, "run_name": "/content/drive/MyDrive/Programming/hf-trainer/t5-v1_1-large-jflAUGv5-training2-ft1-jflAUG-v5", "disable_tqdm": false, "remove_unused_columns": true, "label_names": "None", "load_best_model_at_end": false, "metric_for_best_model": "None", "greater_is_better": "None", "ignore_data_skip": false, "sharded_ddp": "[]", "fsdp": "[]", "fsdp_min_num_params": 0, "deepspeed": "/content/ds_config_zero2.json", "label_smoothing_factor": 0.0, "optim": "adamw_hf", "adafactor": false, "group_by_length": false, "length_column_name": "length", "report_to": "['tensorboard']", "ddp_find_unused_parameters": "None", "ddp_bucket_cap_mb": "None", "dataloader_pin_memory": true, "skip_memory_metrics": true, "use_legacy_prediction_loop": false, "push_to_hub": true, "resume_from_checkpoint": "None", "hub_model_id": "t5-v1_1-large-jflAUGv5-training2-ft1-jflAUG-v5", "hub_strategy": "end", "hub_token": "<HUB_TOKEN>", "hub_private_repo": true, "gradient_checkpointing": true, "include_inputs_for_metrics": false, "fp16_backend": "auto", "push_to_hub_model_id": "None", "push_to_hub_organization": "None", "push_to_hub_token": "<PUSH_TO_HUB_TOKEN>", "_n_gpu": 1, "mp_parameters": "", "auto_find_batch_size": false, "full_determinism": false, "torchdynamo": "None", "ray_scope": "last", "sortish_sampler": false, "predict_with_generate": false, "generation_max_length": "None", "generation_num_beams": "None", "train_batch_size": 4, "eval_batch_size": 1, "configs_src": "t5-v1_1-large-jflAUGv5-training2-ft1-jflAUG-v5"}
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trainer_state.json
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1 |
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