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SChem5Labels-google-t5-v1_1-large-intra_model-sorted-model_annots_str

Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
SChem5Labels-google-t5-v1_1-large-intra_model-sorted-model_annots_str / README.md
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metadata
license: apache-2.0
base_model: google/t5-v1_1-large
tags:
  - generated_from_trainer
model-index:
  - name: SChem5Labels-google-t5-v1_1-large-intra_model
    results: []

SChem5Labels-google-t5-v1_1-large-intra_model

This model is a fine-tuned version of google/t5-v1_1-large on the None dataset. It achieves the following results on the evaluation set:

  • Train Loss: 10.0
  • Loss: nan
  • Losses: [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 200

Training results

Training Loss Epoch Step Train Loss Validation Loss Losses
405015552.0 1.0 99 0.9348 389655456.0 [1, 1, 1, 1, 1, 1, 1, 1, 0.2, 1, 1, 1, 1.0, 1, 1, 0.8, 1, 1.0, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 0.8, 0.4, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 0.8, 1, 1, 1, 0.8, 1, 1, 1.0, 1, 0.6000000000000001, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 0.6000000000000001, 1.0, 1, 1, 0.6000000000000001, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 0.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 0.6000000000000001, 0.8, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1.0, 1, 0.2, 1, 1.0, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 0.8, 1, 1, 0.8, 1, 0.8, 0.8, 1, 1, 1, 0.8, 1.0, 0.4, 0.4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 0.2, 1, 1, 1, 1, 1, 0.4, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.4, 1, 1, 1, 1, 0.8, 1, 1.0, 1, 1, 1, 1, 1, 1, 0.4, 0.2, 1, 0.6000000000000001, 1, 1, 0.8, 1, 0.4, 1, 1, 1, 1, 0.2, 1, 1.0, 0.2, 0.8, 1, 0.4, 1, 1, 1, 1, 1, 0.8, 0.6000000000000001, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 0.6000000000000001, 1, 0.4, 1, 1, 1, 0.6000000000000001, 1, 1, 0.8, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1.0, 0.8, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1.0, 0.6000000000000001, 1, 1, 0.6000000000000001, 1, 1, 1]
414025804.8 2.0 198 0.9816 389655456.0 [1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1.0, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1.0, 1.0, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1.0, 1, 1, 1, 1, 0.2, 1.0, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.4, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 0.8, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.4, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1.0, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
408233932.8 3.0 297 0.9811 389655456.0 [1.0, 1, 1, 1, 1, 1, 1, 1, 0.2, 1, 0.4, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 0.2, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1.0, 1.0, 1, 1, 1, 1, 1, 0.4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1.0, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1.0, 1, 1.0, 1, 1, 1, 1.0, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1.0, 0.2, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 0.8, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.2, 1, 1, 1, 0.8, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1.0, 1, 1, 1, 1, 1.0, 1, 1, 1, 0.4, 1.0, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1.0, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1]
421449804.8 4.0 396 0.9777 389655456.0 [0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 0.4, 1, 1, 1, 0.2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 0.4, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.4, 0.2, 1, 0.8, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.6.1
  • Tokenizers 0.14.1