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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-extraction-cnndm_40000-all-hint_precision-ep50-nonstop
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-extraction-cnndm_40000-all-hint_precision-ep50-nonstop
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6648
- Hint Hit Num: 2.3792
- Hint Precision: 0.4254
- Num: 5.5651
- Gen Len: 18.9983
## 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: 2e-05
- train_batch_size: 60
- eval_batch_size: 400
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Hint Hit Num | Hint Precision | Num | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------------:|:------:|:-------:|
| 2.1311 | 0.75 | 500 | 1.7662 | 2.1274 | 0.4051 | 5.2401 | 18.998 |
| 1.9622 | 1.5 | 1000 | 1.7248 | 2.209 | 0.4135 | 5.3367 | 18.9994 |
| 1.9108 | 2.25 | 1500 | 1.6995 | 2.2522 | 0.4187 | 5.3647 | 18.9999 |
| 1.8855 | 3.0 | 2000 | 1.6806 | 2.246 | 0.4156 | 5.3827 | 18.9999 |
| 1.8518 | 3.75 | 2500 | 1.6778 | 2.2829 | 0.4206 | 5.4082 | 18.9999 |
| 1.8324 | 4.5 | 3000 | 1.6741 | 2.2665 | 0.4175 | 5.4088 | 18.9999 |
| 1.8211 | 5.25 | 3500 | 1.6639 | 2.2819 | 0.4184 | 5.433 | 18.9999 |
| 1.7971 | 6.0 | 4000 | 1.6594 | 2.2896 | 0.4192 | 5.4375 | 18.9999 |
| 1.7788 | 6.75 | 4500 | 1.6554 | 2.3157 | 0.4224 | 5.4634 | 19.0 |
| 1.7755 | 7.5 | 5000 | 1.6550 | 2.3118 | 0.4216 | 5.4629 | 18.9999 |
| 1.7543 | 8.25 | 5500 | 1.6501 | 2.3345 | 0.4235 | 5.4905 | 18.9999 |
| 1.7491 | 9.0 | 6000 | 1.6534 | 2.3242 | 0.422 | 5.4823 | 18.9997 |
| 1.7317 | 9.75 | 6500 | 1.6483 | 2.2962 | 0.4178 | 5.4673 | 18.9999 |
| 1.7239 | 10.49 | 7000 | 1.6539 | 2.3283 | 0.4219 | 5.4958 | 18.9999 |
| 1.7109 | 11.24 | 7500 | 1.6495 | 2.3064 | 0.4198 | 5.4751 | 18.9997 |
| 1.7072 | 11.99 | 8000 | 1.6519 | 2.3465 | 0.4233 | 5.5209 | 18.999 |
| 1.6938 | 12.74 | 8500 | 1.6561 | 2.3086 | 0.4188 | 5.4821 | 18.999 |
| 1.6862 | 13.49 | 9000 | 1.6487 | 2.3524 | 0.423 | 5.5348 | 18.9991 |
| 1.6777 | 14.24 | 9500 | 1.6584 | 2.3453 | 0.4233 | 5.5088 | 18.999 |
| 1.6745 | 14.99 | 10000 | 1.6519 | 2.3062 | 0.418 | 5.4853 | 18.999 |
| 1.6623 | 15.74 | 10500 | 1.6553 | 2.3196 | 0.4202 | 5.4929 | 18.9992 |
| 1.6518 | 16.49 | 11000 | 1.6523 | 2.3467 | 0.4218 | 5.5332 | 18.999 |
| 1.651 | 17.24 | 11500 | 1.6568 | 2.36 | 0.4239 | 5.5397 | 18.999 |
| 1.6446 | 17.99 | 12000 | 1.6574 | 2.3526 | 0.423 | 5.5349 | 18.9991 |
| 1.6334 | 18.74 | 12500 | 1.6632 | 2.3106 | 0.4185 | 5.4907 | 18.9986 |
| 1.6322 | 19.49 | 13000 | 1.6590 | 2.3285 | 0.4199 | 5.5171 | 18.9987 |
| 1.6218 | 20.24 | 13500 | 1.6601 | 2.3377 | 0.4199 | 5.535 | 18.9993 |
| 1.6189 | 20.99 | 14000 | 1.6596 | 2.3493 | 0.4213 | 5.5447 | 18.9987 |
| 1.61 | 21.74 | 14500 | 1.6648 | 2.3792 | 0.4254 | 5.5651 | 18.9983 |
| 1.6064 | 22.49 | 15000 | 1.6668 | 2.3556 | 0.422 | 5.5521 | 18.9979 |
| 1.6004 | 23.24 | 15500 | 1.6674 | 2.3374 | 0.4195 | 5.5356 | 18.9987 |
| 1.597 | 23.99 | 16000 | 1.6654 | 2.3487 | 0.4203 | 5.5595 | 18.9987 |
| 1.5906 | 24.74 | 16500 | 1.6705 | 2.3634 | 0.4227 | 5.5575 | 18.9983 |
| 1.5851 | 25.49 | 17000 | 1.6690 | 2.3609 | 0.4229 | 5.5495 | 18.9983 |
| 1.5856 | 26.24 | 17500 | 1.6716 | 2.3444 | 0.4213 | 5.5376 | 18.9987 |
| 1.577 | 26.99 | 18000 | 1.6708 | 2.3693 | 0.4233 | 5.5631 | 18.9987 |
| 1.5734 | 27.74 | 18500 | 1.6707 | 2.3796 | 0.4236 | 5.5854 | 18.9983 |
| 1.5665 | 28.49 | 19000 | 1.6694 | 2.3639 | 0.4219 | 5.5698 | 18.9987 |
| 1.5666 | 29.24 | 19500 | 1.6798 | 2.3609 | 0.4221 | 5.5592 | 18.9987 |
| 1.564 | 29.99 | 20000 | 1.6778 | 2.3535 | 0.4204 | 5.5679 | 18.9987 |
| 1.5574 | 30.73 | 20500 | 1.6786 | 2.3476 | 0.4196 | 5.564 | 18.9987 |
| 1.5549 | 31.48 | 21000 | 1.6787 | 2.3658 | 0.4213 | 5.5862 | 18.999 |
| 1.5522 | 32.23 | 21500 | 1.6830 | 2.356 | 0.4212 | 5.5619 | 18.999 |
| 1.5485 | 32.98 | 22000 | 1.6784 | 2.3659 | 0.4218 | 5.5768 | 18.9987 |
| 1.5425 | 33.73 | 22500 | 1.6836 | 2.371 | 0.4222 | 5.5849 | 18.998 |
| 1.5449 | 34.48 | 23000 | 1.6817 | 2.365 | 0.4218 | 5.573 | 18.9985 |
| 1.5395 | 35.23 | 23500 | 1.6855 | 2.3633 | 0.4219 | 5.5694 | 18.9984 |
| 1.5358 | 35.98 | 24000 | 1.6834 | 2.3674 | 0.4221 | 5.5788 | 18.9988 |
| 1.5323 | 36.73 | 24500 | 1.6887 | 2.3725 | 0.4225 | 5.5857 | 18.9988 |
| 1.5298 | 37.48 | 25000 | 1.6861 | 2.3656 | 0.4207 | 5.5888 | 18.9991 |
| 1.526 | 38.23 | 25500 | 1.6905 | 2.3535 | 0.4202 | 5.5687 | 18.9991 |
| 1.5329 | 38.98 | 26000 | 1.6890 | 2.371 | 0.4218 | 5.5905 | 18.9988 |
| 1.5254 | 39.73 | 26500 | 1.6885 | 2.371 | 0.4223 | 5.5827 | 18.9989 |
| 1.5245 | 40.48 | 27000 | 1.6908 | 2.3615 | 0.4209 | 5.5781 | 18.9988 |
| 1.5166 | 41.23 | 27500 | 1.6907 | 2.3734 | 0.4214 | 5.598 | 18.9989 |
| 1.5225 | 41.98 | 28000 | 1.6904 | 2.3739 | 0.4219 | 5.5945 | 18.9989 |
| 1.5149 | 42.73 | 28500 | 1.6916 | 2.3768 | 0.4229 | 5.5913 | 18.9989 |
| 1.5178 | 43.48 | 29000 | 1.6938 | 2.3654 | 0.4214 | 5.5826 | 18.9991 |
| 1.5212 | 44.23 | 29500 | 1.6928 | 2.3674 | 0.4219 | 5.5821 | 18.9988 |
| 1.5136 | 44.98 | 30000 | 1.6917 | 2.3781 | 0.4227 | 5.5952 | 18.999 |
| 1.5097 | 45.73 | 30500 | 1.6923 | 2.3704 | 0.4218 | 5.5896 | 18.999 |
| 1.5183 | 46.48 | 31000 | 1.6935 | 2.3719 | 0.4217 | 5.5931 | 18.999 |
| 1.5092 | 47.23 | 31500 | 1.6935 | 2.3684 | 0.4216 | 5.5868 | 18.999 |
| 1.5127 | 47.98 | 32000 | 1.6943 | 2.3691 | 0.4218 | 5.5863 | 18.999 |
| 1.5124 | 48.73 | 32500 | 1.6940 | 2.3704 | 0.422 | 5.5874 | 18.9987 |
| 1.5117 | 49.48 | 33000 | 1.6948 | 2.3693 | 0.4217 | 5.587 | 18.9987 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
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