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owanr
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SChem5Labels-google-t5-v1_1-base-intra_model

Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
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End of training

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  1. README.md +11 -11
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -15,9 +15,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Train Loss: 0.6872
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  - Loss: nan
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- - Losses: [1, 1, 0.6000000000000001, 1, 0.8, 1, 1, 0.4, 1, 0.6000000000000001, 1, 0.8, 0.6000000000000001, 0.2, 0.8, 0.4, 1, 0.6000000000000001, 0.2, 1, 1, 1, 1, 1.0, 1, 0.8, 1, 1, 0.2, 1.0, 0.6000000000000001, 1, 0.8, 0.8, 1, 0.4, 1, 1, 1, 0.8, 0.4, 1.0, 1, 0.6000000000000001, 0.2, 0.2, 1, 1, 0.4, 0.4, 1, 0.4, 0.8, 0.8, 1, 0.8, 1, 0.4, 1, 1, 0.8, 0.8, 0.0, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.0, 1, 0.4, 0.8, 0.8, 0.8, 0.0, 0.4, 0.4, 0.8, 0.4, 0.8, 1, 0.8, 0.0, 1, 1, 0.6000000000000001, 0.8, 0.4, 0.6000000000000001, 1, 0.8, 0.0, 0.8, 0.0, 0.4, 0.0, 0.0, 0.0, 0.8, 1, 0.2, 1, 0.4, 0.8, 0.4, 1, 0.4, 0.6000000000000001, 1, 0.6000000000000001, 0.8, 0.4, 0.8, 1, 0.2, 0.8, 0.4, 0.8, 0.0, 0.8, 1, 1, 0.0, 1, 0.8, 0.0, 0.0, 1, 0.0, 0.0, 1, 0.4, 1, 1, 1, 0.8, 1, 0.4, 1, 0.8, 1, 0.8, 0.4, 0.4, 0.0, 0.8, 0.4, 1, 0.0, 1, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.6000000000000001, 0.0, 1, 0.2, 0.8, 0.8, 0.8, 0.4, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 0.4, 0.0, 1, 1, 1, 1, 1, 1, 0.8, 1, 0.0, 0.6000000000000001, 0.0, 0.4, 0.0, 1, 0.0, 1, 1, 0.6000000000000001, 1, 0.8, 0.0, 0.8, 1.0, 0.4, 0.8, 1, 0.4, 0.8, 0.4, 0.0, 0.6000000000000001, 0.8, 0.8, 0.8, 1, 0.8, 0.4, 0.6000000000000001, 0.8, 0.8, 1.0, 1, 0.4, 1, 0.2, 0.4, 1, 0.8, 1, 1.0, 0.4, 0.4, 1, 0.4, 0.2, 1.0, 1, 1, 0.4, 0.4, 0.4, 1.0, 1.0, 0.4, 0.8, 0.0, 1, 1, 1, 1, 0.8, 1.0, 0.2, 0.2, 0.6000000000000001, 0.8, 0.8, 1, 1.0, 1, 1.0, 1, 1.0, 1.0, 0.8, 1, 0.2, 0.2, 1.0, 0.6000000000000001, 1, 0.2, 0.4, 0.2, 0.4, 0.4, 0.8, 0.8, 1, 0.8, 1, 1, 0.0, 0.4, 1, 0.8, 1, 0.2, 0.6000000000000001, 0.6000000000000001, 1, 0.6000000000000001, 0.0, 1, 0.8, 0.4, 0.0, 0.4, 0.8, 0.4, 0.8, 0.8, 1, 0.8, 1, 0.4, 0.8, 0.4, 0.4, 1, 1, 1, 0.4, 0.2, 0.4, 1, 1, 0.4, 1.0, 1.0, 0.8, 0.6000000000000001, 0.2, 0.8, 0.2, 0.8, 0.6000000000000001, 0.8, 1, 1, 0.4, 1, 0.8, 0.0, 0.2, 0.4, 0.4, 1, 1, 1, 0.0, 0.6000000000000001, 1, 0.6000000000000001, 0.2, 1, 0.6000000000000001, 1, 0.8, 1, 0.8, 0.2, 1, 1, 0.2, 0.4, 0.4, 1, 0.0, 1, 1]
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  ## Model description
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@@ -42,18 +42,18 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 30
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  ### Training results
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- | Training Loss | Epoch | Step | Train Loss | Validation Loss | Losses |
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- |:-------------:|:-----:|:----:|:----------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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- | 15.4391 | 1.0 | 99 | 0.8139 | 12.2133 | [1, 0.8, 0.8, 1, 0.4, 1, 1, 1, 1.0, 1.0, 1, 1, 1, 1, 1, 1, 0.2, 1, 1, 0.8, 0.8, 1, 0.4, 1, 1.0, 1, 0.8, 1.0, 1, 0.2, 1, 0.8, 1, 1.0, 1, 1, 1, 1, 0.2, 1, 1, 0.4, 0.8, 0.8, 1, 1, 0.8, 1, 1, 1, 0.4, 1, 1, 1.0, 0.2, 1, 1, 1, 0.2, 0.4, 1, 0.0, 1, 0.2, 0.2, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1.0, 0.8, 1, 0.8, 1, 1, 0.6000000000000001, 0.8, 0.8, 1, 1, 0.2, 1, 1, 1, 0.2, 1, 0.2, 0.8, 1, 0.2, 0.2, 1, 1, 1.0, 0.8, 1, 1, 0.4, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 0.8, 1, 1, 0.0, 0.4, 1.0, 1, 0.2, 1, 1, 1.0, 1, 0.6000000000000001, 0.4, 0.0, 0.4, 0.8, 1.0, 0.6000000000000001, 1, 0.6000000000000001, 1, 1, 0.8, 1, 1, 1, 1, 0.4, 1, 1, 0.2, 1, 1, 1.0, 0.8, 1, 0.8, 1, 0.4, 1, 0.6000000000000001, 1, 1, 1, 0.8, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 0.0, 1, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 1, 0.8, 0.2, 0.8, 1, 0.4, 0.8, 1, 1.0, 1, 0.8, 1, 0.8, 1, 0.4, 0.2, 1, 0.0, 1.0, 0.8, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 0.4, 1, 0.0, 0.8, 0.8, 0.8, 0.0, 0.2, 0.8, 0.2, 0.4, 1, 0.6000000000000001, 1.0, 0.8, 1, 1, 0.6000000000000001, 1, 0.6000000000000001, 0.4, 1, 1, 0.8, 1, 1, 1, 0.2, 1, 1, 0.8, 1, 1, 1, 1.0, 1, 1, 1, 0.8, 0.6000000000000001, 0.4, 1, 1, 0.8, 0.4, 1, 0.8, 1, 1.0, 0.2, 1, 1, 1, 1, 1, 0.4, 0.8, 1, 1.0, 1, 1, 1, 1, 0.6000000000000001, 1, 0.4, 0.8, 0.4, 0.8, 1, 1.0, 1.0, 1, 1, 0.2, 1, 1, 1, 0.6000000000000001, 1, 0.4, 1, 0.0, 0.8, 0.6000000000000001, 1, 1.0, 0.6000000000000001, 1, 1.0, 0.8, 0.2, 1.0, 1, 1.0, 1, 1, 0.4, 1, 1.0, 0.8, 0.8, 0.2, 1, 1, 0.8, 1, 0.4, 1, 0.8, 1, 1, 1, 0.6000000000000001, 1, 1.0, 1.0, 0.8, 0.2, 1, 1.0, 0.8, 0.6000000000000001, 0.2, 1, 0.6000000000000001, 1, 1, 0.8, 0.8, 1, 1, 0.6000000000000001, 0.4, 0.2, 1, 1, 1, 0.4, 1.0, 1, 0.4, 0.2, 1, 1, 1, 0.6000000000000001, 0.8, 0.2, 1.0, 1, 0.8, 1, 1, 1, 0.6000000000000001, 0.8, 1.0] |
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- | 8.6424 | 2.0 | 198 | 0.7276 | 7.1069 | [0.8, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 0.8, 1, 1, 1.0, 0.8, 0.6000000000000001, 0.6000000000000001, 0.4, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.2, 0.8, 0.8, 0.8, 0.8, 0.4, 1, 1, 0.8, 0.8, 0.2, 0.8, 0.8, 0.4, 0.8, 1, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.4, 1, 0.4, 0.8, 0.2, 0.8, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.2, 1, 0.8, 1, 0.8, 0.4, 0.8, 0.0, 0.8, 0.8, 0.0, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 1, 1, 0.8, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.4, 0.0, 1.0, 1, 0.8, 0.8, 0.6000000000000001, 0.4, 1, 1, 0.6000000000000001, 0.0, 0.0, 0.8, 1, 0.2, 0.8, 0.6000000000000001, 0.8, 1, 0.8, 0.8, 1, 0.8, 1.0, 0.4, 0.8, 0.8, 0.8, 0.8, 0.8, 1.0, 0.8, 0.8, 0.8, 1, 1, 1, 1.0, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.4, 1, 1.0, 0.8, 0.8, 0.0, 0.4, 1, 1, 1, 1, 0.8, 0.6000000000000001, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 0.6000000000000001, 0.8, 0.4, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.4, 0.2, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.2, 0.4, 0.8, 0.6000000000000001, 0.6000000000000001, 0.0, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 1, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.8, 0.6000000000000001, 1, 1, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 1, 0.8, 0.8, 0.8, 1.0, 0.8, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.4, 0.8, 0.8, 0.6000000000000001, 0.8, 0.2, 0.8, 1, 0.6000000000000001, 0.8, 0.0, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 1, 1, 1, 0.8, 0.6000000000000001, 1, 0.8, 0.6000000000000001, 0.8, 0.0, 1, 0.8, 0.0, 0.6000000000000001, 0.8, 1.0, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.0, 1, 0.8, 0.4, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 1, 0.8, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.0, 0.8, 1, 0.8, 0.0, 0.2, 0.8, 0.4, 0.8, 1, 1, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.0, 1, 1.0, 0.0, 0.0, 0.8, 0.8, 0.2, 0.0, 0.2, 0.6000000000000001, 1, 1, 0.8, 0.8, 1, 1.0, 0.0, 0.8, 1.0, 0.8, 0.8, 1, 0.8, 0.4, 0.6000000000000001, 0.4, 1] |
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- | 4.6818 | 3.0 | 297 | 0.6730 | 3.1929 | [0.4, 1, 0.4, 0.4, 0.4, 0.6000000000000001, 0.8, 1, 0.6000000000000001, 0.6000000000000001, 1, 0.8, 1, 1, 1.0, 0.6000000000000001, 0.0, 0.6000000000000001, 0.6000000000000001, 0.8, 0.2, 0.4, 0.4, 0.8, 0.6000000000000001, 1, 1, 0.8, 0.6000000000000001, 0.4, 0.4, 0.8, 0.8, 0.0, 1, 1, 0.4, 0.4, 0.6000000000000001, 1, 1, 0.0, 1, 1, 0.4, 1, 1, 0.8, 1, 0.6000000000000001, 0.8, 0.4, 0.4, 0.8, 0.4, 0.8, 1, 1, 0.4, 0.0, 0.6000000000000001, 0.0, 1, 0.6000000000000001, 0.4, 1, 0.2, 0.8, 1, 1, 1, 0.6000000000000001, 1, 0.8, 0.8, 0.8, 1, 1, 1, 0.4, 1, 0.4, 0.4, 0.2, 0.6000000000000001, 1, 0.6000000000000001, 0.6000000000000001, 0.4, 1, 0.4, 1, 0.4, 0.4, 0.4, 1, 0.8, 0.6000000000000001, 0.4, 1, 0.4, 0.6000000000000001, 0.8, 1, 1, 0.4, 0.6000000000000001, 1, 0.8, 0.8, 1, 0.6000000000000001, 1, 0.0, 0.4, 1, 1, 0.4, 1, 1.0, 0.0, 1.0, 0.6000000000000001, 1.0, 0.0, 0.4, 1, 1, 0.6000000000000001, 1, 0.6000000000000001, 1, 0.6000000000000001, 0.8, 1, 1, 1, 0.4, 0.0, 0.8, 0.4, 0.8, 0.8, 1, 0.4, 1, 1, 1, 0.8, 0.6000000000000001, 1, 0.4, 0.8, 1, 1, 1, 1, 0.8, 1.0, 0.4, 1, 0.4, 1, 1.0, 0.4, 1, 0.4, 0.0, 1, 0.8, 1, 0.6000000000000001, 1, 0.6000000000000001, 0.8, 0.6000000000000001, 0.4, 0.4, 0.4, 0.8, 0.6000000000000001, 1, 0.4, 1, 1, 0.6000000000000001, 0.4, 0.6000000000000001, 0.0, 0.6000000000000001, 0.6000000000000001, 0.2, 1, 1, 0.6000000000000001, 0.6000000000000001, 0.8, 0.8, 0.2, 1, 0.6000000000000001, 0.0, 0.8, 0.0, 0.4, 0.6000000000000001, 1, 0.2, 0.0, 0.6000000000000001, 0.2, 0.0, 1, 0.0, 1.0, 0.4, 1, 1, 0.8, 1, 0.6000000000000001, 0.0, 0.4, 1, 1, 0.6000000000000001, 0.6000000000000001, 1, 0.2, 1, 1, 1, 1, 0.4, 0.4, 0.6000000000000001, 1, 1, 1, 1, 0.4, 0.8, 1, 0.4, 0.8, 0.4, 1, 0.4, 1, 1, 0.0, 1, 1, 0.2, 0.8, 0.4, 0.0, 1, 0.8, 1.0, 1, 0.6000000000000001, 0.4, 1, 0.6000000000000001, 1, 0.4, 1, 0.8, 0.8, 1, 0.6000000000000001, 1, 0.6000000000000001, 1, 0.4, 0.6000000000000001, 0.6000000000000001, 0.8, 0.6000000000000001, 1, 0.4, 0.6000000000000001, 0.2, 0.4, 1.0, 0.2, 1, 0.6000000000000001, 0.4, 0.8, 0.4, 0.4, 1, 1, 0.8, 0.8, 1, 0.4, 1, 0.8, 0.4, 0.8, 0.6000000000000001, 0.2, 0.4, 0.4, 0.4, 1, 0.4, 0.6000000000000001, 0.0, 1, 1, 0.4, 0.6000000000000001, 1, 0.6000000000000001, 0.4, 0.2, 0.4, 0.0, 0.8, 1, 0.4, 0.8, 0.0, 0.4, 1, 0.4, 1, 0.4, 1, 1.0, 0.4, 0.4, 1, 0.4, 0.6000000000000001, 0.4, 0.6000000000000001, 0.6000000000000001, 0.8, 0.4, 0.8, 1, 1, 1.0, 0.4, 0.4, 1.0, 0.4, 0.6000000000000001, 0.6000000000000001, 1, 0.0, 0.6000000000000001, 0.4, 1] |
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- | 3.4662 | 4.0 | 396 | 0.7259 | 1.9561 | [0.8, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 0.8, 1, 1, 1.0, 0.8, 0.6000000000000001, 0.6000000000000001, 0.4, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.2, 0.8, 0.8, 0.8, 0.8, 0.4, 1, 1, 0.8, 0.8, 0.2, 0.8, 0.8, 0.4, 0.8, 1, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.4, 1, 0.4, 0.8, 0.2, 0.8, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.2, 1, 0.8, 1, 0.8, 0.4, 0.8, 0.0, 0.8, 0.8, 0.0, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 1, 1, 0.8, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.4, 0.0, 1.0, 1, 0.8, 0.8, 0.6000000000000001, 0.4, 1, 1, 0.6000000000000001, 0.0, 0.0, 0.8, 1, 0.2, 0.8, 0.6000000000000001, 0.8, 1, 0.8, 0.8, 1, 0.8, 1.0, 0.4, 0.8, 0.8, 0.8, 0.8, 0.8, 1.0, 0.8, 0.8, 0.8, 1, 1, 1, 1.0, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.4, 1, 1.0, 0.8, 0.8, 0.0, 0.4, 1, 1, 1, 1, 0.8, 0.6000000000000001, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 0.6000000000000001, 0.8, 0.4, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.4, 0.2, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.2, 0.4, 0.8, 0.6000000000000001, 0.6000000000000001, 0.0, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 1, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.8, 0.6000000000000001, 1, 1, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 1, 0.8, 0.8, 0.8, 1.0, 0.8, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.4, 0.8, 0.8, 0.6000000000000001, 0.8, 0.2, 0.8, 1, 0.6000000000000001, 0.8, 0.0, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 1, 1, 1, 0.8, 0.6000000000000001, 1, 0.8, 0.6000000000000001, 0.8, 0.0, 1, 0.8, 0.0, 0.6000000000000001, 0.8, 1.0, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.0, 1, 0.2, 0.4, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 1, 0.8, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.0, 0.8, 1, 0.8, 0.0, 0.2, 0.8, 0.4, 0.8, 1, 1, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.0, 1, 1.0, 0.0, 0.0, 0.8, 0.8, 0.2, 0.0, 0.2, 0.6000000000000001, 1, 1, 0.8, 0.8, 1, 1.0, 0.0, 0.8, 1.0, 0.8, 0.8, 1, 0.8, 0.4, 0.6000000000000001, 0.4, 1] |
55
- | 2.583 | 5.0 | 495 | 0.6780 | 1.4890 | [0.4, 1, 0.4, 0.4, 0.4, 1.0, 0.8, 1, 0.6000000000000001, 0.6000000000000001, 1, 0.8, 1, 1, 1, 0.8, 0.0, 0.6000000000000001, 0.6000000000000001, 1, 0.2, 0.0, 0.4, 0.8, 0.6000000000000001, 1, 1, 1, 0.6000000000000001, 0.4, 0.4, 1.0, 0.8, 0.6000000000000001, 1, 1, 0.0, 0.4, 0.6000000000000001, 1, 1, 0.0, 1, 0.8, 0.4, 1, 1, 0.8, 1, 0.6000000000000001, 0.8, 0.4, 0.4, 0.8, 0.4, 0.8, 1, 1, 0.4, 0.0, 0.6000000000000001, 0.0, 1, 0.6000000000000001, 0.4, 1, 0.2, 0.2, 1, 1, 1, 0.6000000000000001, 1, 0.8, 0.6000000000000001, 0.8, 1, 1, 1, 0.4, 1, 0.4, 0.4, 0.2, 0.6000000000000001, 1, 0.6000000000000001, 0.6000000000000001, 0.4, 1, 0.4, 1, 0.4, 0.4, 0.4, 1, 0.8, 0.6000000000000001, 0.4, 1, 0.4, 0.6000000000000001, 0.8, 1, 1, 0.4, 0.6000000000000001, 1, 1, 0.8, 1, 0.6000000000000001, 1, 0.0, 0.4, 1, 1, 0.2, 1, 1, 0.0, 1.0, 0.6000000000000001, 1, 0.4, 0.4, 1, 1, 0.6000000000000001, 1, 0.8, 1, 0.6000000000000001, 0.8, 1, 1, 1, 0.4, 0.0, 0.8, 0.4, 0.2, 1, 1, 0.4, 1, 1, 1, 0.8, 0.6000000000000001, 1, 0.4, 1, 1, 1, 1, 1, 0.8, 1.0, 0.4, 1, 0.4, 1, 0.4, 0.4, 1, 0.4, 0.0, 1, 0.6000000000000001, 1, 0.6000000000000001, 1, 0.6000000000000001, 0.8, 0.6000000000000001, 0.4, 0.4, 0.4, 0.8, 0.6000000000000001, 1, 0.4, 1, 1, 0.6000000000000001, 0.8, 0.6000000000000001, 0.0, 1, 0.0, 0.2, 1, 1, 0.6000000000000001, 0.6000000000000001, 0.8, 1, 0.2, 1, 0.6000000000000001, 0.0, 0.8, 0.0, 0.2, 0.6000000000000001, 1, 0.2, 0.0, 0.6000000000000001, 0.2, 0.0, 1, 0.0, 1, 0.4, 1, 1, 0.8, 1, 0.0, 0.0, 0.4, 1, 1, 0.6000000000000001, 0.6000000000000001, 1, 0.2, 1, 1, 1, 1, 0.8, 0.4, 0.6000000000000001, 1, 1, 1, 1, 0.4, 0.8, 1, 0.4, 0.8, 0.2, 1, 0.4, 1, 1, 0.0, 1, 1, 0.2, 0.8, 0.2, 0.6000000000000001, 1, 1, 1.0, 1, 0.6000000000000001, 0.0, 1, 0.6000000000000001, 1, 0.8, 1, 0.8, 0.8, 1, 0.6000000000000001, 1, 0.0, 0.6000000000000001, 0.4, 0.6000000000000001, 0.6000000000000001, 1, 0.6000000000000001, 1, 0.4, 0.6000000000000001, 0.4, 0.4, 1.0, 0.2, 1, 0.6000000000000001, 0.4, 0.8, 0.4, 0.4, 1, 1, 1, 0.8, 1, 0.4, 1, 0.4, 0.4, 0.8, 1.0, 0.2, 0.4, 0.4, 0.4, 1, 0.2, 0.6000000000000001, 0.8, 1, 1, 0.4, 0.6000000000000001, 1, 0.6000000000000001, 0.8, 0.2, 0.4, 0.0, 0.8, 1, 0.4, 0.8, 0.8, 0.2, 1, 0.4, 1, 0.4, 1, 1.0, 1, 0.4, 1, 0.4, 0.6000000000000001, 0.4, 0.6000000000000001, 0.6000000000000001, 1, 0.4, 1, 1, 1, 1.0, 0.4, 0.4, 0.4, 0.2, 1.0, 0.6000000000000001, 1, 0.0, 0.6000000000000001, 0.4, 1] |
56
- | 2.0155 | 6.0 | 594 | 0.6747 | 0.9281 | [0.4, 1, 0.0, 0.4, 0.4, 1.0, 0.8, 1, 0.6000000000000001, 1.0, 1, 0.8, 1, 1, 1, 0.6000000000000001, 0.0, 0.0, 0.6000000000000001, 1, 0.2, 0.0, 0.4, 0.8, 0.6000000000000001, 1, 1, 1, 1, 0.0, 0.0, 0.8, 0.8, 0.4, 1, 1, 0.0, 0.4, 0.6000000000000001, 1, 1, 0.4, 1, 0.8, 0.4, 1, 1, 0.8, 1, 0.6000000000000001, 1, 0.0, 0.0, 0.8, 0.0, 1, 1, 1, 0.0, 0.4, 0.2, 0.4, 1, 0.6000000000000001, 0.0, 1, 0.2, 0.0, 1, 1, 1, 1.0, 1, 0.8, 0.6000000000000001, 0.8, 1, 1, 1, 0.4, 1, 0.0, 0.4, 0.2, 0.2, 1, 0.2, 1.0, 0.4, 1, 0.4, 1, 0.0, 0.8, 0.0, 1, 0.8, 0.6000000000000001, 0.4, 1, 0.4, 0.2, 0.8, 1, 1, 0.0, 0.6000000000000001, 1, 1, 0.8, 1, 0.2, 1, 0.4, 0.8, 1, 1, 0.0, 1, 1.0, 0.0, 0.6000000000000001, 0.6000000000000001, 1, 0.8, 0.4, 1, 1, 1, 1, 1.0, 1, 0.2, 0.8, 1, 1, 1, 0.4, 0.0, 0.8, 0.4, 0.2, 1, 1, 0.4, 1, 1, 1, 0.8, 0.6000000000000001, 1, 0.4, 1, 1, 1, 1, 1, 1, 1.0, 0.4, 1, 0.4, 1, 0.4, 0.0, 1, 0.8, 0.0, 1, 0.4, 1, 0.6000000000000001, 1, 0.6000000000000001, 0.8, 0.6000000000000001, 0.4, 0.0, 0.4, 1, 1.0, 1, 0.4, 1, 1, 0.2, 0.8, 0.6000000000000001, 0.4, 1, 0.6000000000000001, 0.2, 1, 1, 0.2, 0.6000000000000001, 0.8, 1, 0.2, 1, 0.2, 0.0, 0.8, 0.0, 0.4, 1.0, 1, 0.2, 0.0, 0.6000000000000001, 0.8, 0.0, 1, 0.6000000000000001, 1.0, 0.8, 1, 1, 1, 1, 0.2, 0.0, 0.4, 1, 1, 0.6000000000000001, 0.6000000000000001, 1, 0.2, 1, 1, 1, 1, 0.4, 0.0, 0.6000000000000001, 1, 1, 1, 1, 0.4, 0.8, 1, 0.4, 0.8, 0.0, 1, 0.4, 1, 1, 0.6000000000000001, 1, 1, 0.2, 0.8, 0.0, 0.4, 1, 1, 1.0, 1, 0.6000000000000001, 0.0, 1, 0.6000000000000001, 1, 0.8, 1, 1, 0.8, 1, 0.6000000000000001, 1, 0.6000000000000001, 0.6000000000000001, 0.4, 0.6000000000000001, 0.2, 1, 0.6000000000000001, 1, 0.8, 0.2, 0.4, 0.4, 1, 0.2, 1, 0.6000000000000001, 0.4, 1, 0.4, 0.4, 1, 1, 1, 1, 1, 0.4, 1, 0.2, 0.4, 0.8, 0.6000000000000001, 0.2, 0.4, 0.8, 0.4, 1, 0.0, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.8, 0.6000000000000001, 1, 1.0, 0.8, 0.2, 0.0, 0.0, 0.8, 1, 0.4, 0.4, 0.0, 0.0, 1, 0.0, 1, 0.8, 1, 1, 0.8, 0.8, 1, 0.4, 1, 0.8, 1, 0.6000000000000001, 1, 0.4, 1, 1, 1, 1.0, 0.8, 0.4, 0.4, 0.0, 1.0, 0.2, 1, 0.4, 0.6000000000000001, 0.4, 1] |
57
 
58
 
59
  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Train Loss: 0.9822
19
  - Loss: nan
20
+ - Losses: [1, 1, 0.6000000000000001, 1, 0.8, 1, 1, 0.4, 1, 0.6000000000000001, 1, 0.8, 0.6000000000000001, 0.8, 1.0, 0.4, 1, 0.6000000000000001, 0.8, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 0.8, 1.0, 0.6000000000000001, 1, 0.4, 0.4, 1, 0.4, 1, 1, 1, 0.8, 0.4, 1, 1, 1, 0.8, 0.8, 1, 1, 0.4, 0.4, 1, 0.4, 0.8, 0.8, 1, 0.8, 1, 0.0, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 1.0, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.4, 1, 0.4, 0.4, 0.8, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.4, 0.8, 0.8, 0.6000000000000001, 0.4, 0.0, 0.6000000000000001, 0.8, 0.8, 0.4, 0.4, 0.4, 0.6000000000000001, 0.0, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 0.0, 0.4, 0.4, 1, 0.0, 0.6000000000000001, 0.8, 0.6000000000000001, 0.8, 0.4, 0.4, 0.8, 0.8, 0.4, 0.4, 0.8, 0.4, 0.8, 0.8, 1, 0.4, 0.8, 0.8, 0.4, 0.4, 0.8, 0.4, 0.4, 1, 0.4, 0.8, 0.8, 0.8, 0.8, 0.8, 0.4, 0.8, 0.8, 0.8, 0.8, 0.4, 0.4, 0.4, 0.8, 0.4, 0.8, 0.4, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.6000000000000001, 0.4, 1, 0.4, 1, 0.8, 0.8, 0.4, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 1, 0.8, 1, 0.4, 0.4, 1, 0.8, 0.8, 1, 1, 1, 0.8, 1, 0.4, 0.6000000000000001, 0.4, 0.4, 0.4, 1, 0.4, 0.8, 0.8, 0.6000000000000001, 0.8, 0.8, 0.4, 0.8, 1, 0.8, 0.8, 1, 1, 0.4, 0.4, 0.4, 0.6000000000000001, 0.8, 0.8, 0.8, 1, 0.8, 0.4, 0.8, 1.0, 0.8, 1.0, 1, 0.4, 0.8, 0.8, 1, 1, 0.8, 1, 1.0, 1, 0.4, 1, 0.6000000000000001, 0.8, 1, 1.0, 1, 0.6000000000000001, 0.4, 0.4, 0.6000000000000001, 1.0, 0.8, 0.8, 0.4, 1, 1, 1, 0.8, 0.8, 1.0, 0.8, 0.8, 0.6000000000000001, 0.8, 0.4, 0.8, 1, 1, 1.0, 0.8, 1.0, 1.0, 0.8, 1, 0.8, 0.8, 1.0, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 1, 0.4, 0.8, 0.4, 0.8, 0.8, 1, 1, 0.4, 0.4, 1, 0.8, 1, 0.8, 0.6000000000000001, 0.6000000000000001, 1, 0.6000000000000001, 0.4, 1, 0.8, 0.4, 0.4, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 0.8, 0.4, 0.4, 1, 1, 1, 0.4, 0.8, 0.4, 1, 1, 0.4, 1.0, 1.0, 0.4, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.4, 0.8, 0.4, 0.0, 0.8, 0.4, 0.4, 0.8, 1, 1, 0.4, 0.6000000000000001, 1, 0.6000000000000001, 0.8, 1, 0.6000000000000001, 1.0, 1, 1.0, 0.4, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]
21
 
22
  ## Model description
23
 
 
42
  - seed: 42
43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
44
  - lr_scheduler_type: linear
45
+ - num_epochs: 200
46
 
47
  ### Training results
48
 
49
+ | Training Loss | Epoch | Step | Train Loss | Validation Loss | Losses |
50
+ |:-------------:|:-----:|:----:|:----------:|:---------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
51
+ | 405015552.0 | 1.0 | 99 | 0.8747 | 389655488.0 | [0.8, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 0.8, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.6000000000000001, 0.8, 0.8, 1, 1, 1, 0.8, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1.0, 0.4, 1, 1, 0.8, 0.2, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1.0, 1, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 0.6000000000000001, 1, 1, 1, 0.8, 1, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 0.8, 1, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 1, 1, 0.8, 1, 1, 1, 0.8, 0.0, 1, 0.4, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1.0, 1, 0.8, 1, 1, 0.0, 1, 1, 1, 1, 1, 0.8, 1.0, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 0.0, 1.0, 1, 1, 0.2, 1, 1.0, 1, 1, 1, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 0.8, 0.2, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 0.8, 0.8, 0.8, 0.8, 1, 1.0, 1, 1, 1, 1, 0.0, 1, 1, 0.6000000000000001, 0.6000000000000001, 0.8, 1, 0.8, 0.8, 1, 1, 1.0, 0.4, 0.6000000000000001, 1, 0.8, 0.8, 0.4, 1, 0.4, 1, 1, 0.4, 1, 1, 1.0, 1, 1, 0.8, 1.0, 0.8, 1, 1, 1, 0.4, 0.2, 0.8, 1, 0.4, 0.8, 1, 1, 0.8, 0.8, 1, 1, 0.6000000000000001, 1, 0.4, 1, 1, 1, 1, 1, 1, 1.0, 0.8, 1, 1, 1, 1.0, 1, 1, 0.8, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 0.4, 1, 0.4, 1, 1, 0.6000000000000001, 1.0, 1, 0.8, 1, 0.6000000000000001, 0.8, 1, 1, 1, 1, 1, 1, 1.0, 0.6000000000000001, 1.0, 0.4, 1, 1, 0.6000000000000001, 1, 0.8, 0.8, 1, 1, 1, 1, 0.6000000000000001, 1, 1.0, 0.4, 1, 1, 0.8, 0.6000000000000001, 1, 1, 1, 1, 0.2, 0.4, 1, 1.0, 1, 0.8, 0.8, 0.8, 1, 1.0, 1, 0.4, 1, 1, 0.6000000000000001, 1, 0.4, 1, 0.8, 0.6000000000000001, 1, 1, 0.0, 0.8, 1, 1, 0.6000000000000001, 0.6000000000000001, 1, 0.8, 1, 1, 1, 1, 0.4, 1, 0.4, 1, 1, 1, 1, 0.4, 0.6000000000000001, 1, 1, 1, 0.8, 1.0, 1, 1.0, 1, 1.0, 1, 1, 0.2, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 0.4, 1, 1, 1, 1, 1, 1, 0.0, 0.8, 0.6000000000000001, 1] |
52
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53
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56
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57
 
58
 
59
  ### Framework versions
pytorch_model.bin CHANGED
@@ -1,3 +1,3 @@
1
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