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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-extraction-cnndm_20000-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_20000-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: 1.6910
- Hint Hit Num: 2.3981
- Hint Precision: 0.431
- Num: 5.5422
- Gen Len: 18.9991
## 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: 16
- eval_batch_size: 200
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------------:|:------:|:-------:|
| 2.0952 | 0.8 | 1000 | 1.7450 | 2.2694 | 0.4207 | 5.4015 | 18.9993 |
| 1.9364 | 1.6 | 2000 | 1.7131 | 2.2371 | 0.4159 | 5.3613 | 19.0 |
| 1.8853 | 2.4 | 3000 | 1.7026 | 2.2893 | 0.4213 | 5.4161 | 18.9999 |
| 1.8383 | 3.2 | 4000 | 1.6955 | 2.2796 | 0.4206 | 5.404 | 18.9999 |
| 1.8087 | 4.0 | 5000 | 1.6866 | 2.3175 | 0.4244 | 5.4464 | 18.9996 |
| 1.7778 | 4.8 | 6000 | 1.6829 | 2.3311 | 0.423 | 5.4935 | 18.9996 |
| 1.7412 | 5.6 | 7000 | 1.6863 | 2.3112 | 0.4205 | 5.4712 | 18.9998 |
| 1.732 | 6.4 | 8000 | 1.6903 | 2.3108 | 0.4209 | 5.4711 | 18.999 |
| 1.6993 | 7.2 | 9000 | 1.6840 | 2.3855 | 0.4289 | 5.5382 | 18.9998 |
| 1.688 | 8.0 | 10000 | 1.6870 | 2.3089 | 0.4191 | 5.4856 | 18.9995 |
| 1.6609 | 8.8 | 11000 | 1.6910 | 2.3981 | 0.431 | 5.5422 | 18.9991 |
| 1.6462 | 9.6 | 12000 | 1.7011 | 2.3492 | 0.4237 | 5.5193 | 18.9994 |
| 1.6302 | 10.4 | 13000 | 1.7008 | 2.3825 | 0.4279 | 5.5488 | 18.999 |
| 1.6108 | 11.2 | 14000 | 1.7058 | 2.3274 | 0.4217 | 5.496 | 18.9998 |
| 1.6031 | 12.0 | 15000 | 1.7092 | 2.3741 | 0.4265 | 5.5432 | 18.999 |
| 1.5798 | 12.8 | 16000 | 1.7112 | 2.3416 | 0.4217 | 5.5248 | 18.9981 |
| 1.5664 | 13.6 | 17000 | 1.7210 | 2.4102 | 0.4291 | 5.6002 | 18.9986 |
| 1.5521 | 14.4 | 18000 | 1.7193 | 2.3779 | 0.4236 | 5.5859 | 18.9992 |
| 1.5426 | 15.2 | 19000 | 1.7323 | 2.3727 | 0.4227 | 5.5887 | 18.9992 |
| 1.5318 | 16.0 | 20000 | 1.7214 | 2.3992 | 0.4274 | 5.593 | 18.999 |
| 1.5134 | 16.8 | 21000 | 1.7300 | 2.4111 | 0.4272 | 5.6176 | 18.9987 |
| 1.5031 | 17.6 | 22000 | 1.7363 | 2.3823 | 0.425 | 5.5836 | 18.999 |
| 1.4845 | 18.4 | 23000 | 1.7429 | 2.4123 | 0.428 | 5.6077 | 18.9987 |
| 1.4895 | 19.2 | 24000 | 1.7534 | 2.3726 | 0.4207 | 5.6104 | 18.9984 |
| 1.4687 | 20.0 | 25000 | 1.7552 | 2.4185 | 0.4268 | 5.6374 | 18.9987 |
| 1.4601 | 20.8 | 26000 | 1.7602 | 2.3924 | 0.4251 | 5.6007 | 18.9989 |
| 1.4486 | 21.6 | 27000 | 1.7687 | 2.3863 | 0.4234 | 5.6088 | 18.9984 |
| 1.4353 | 22.4 | 28000 | 1.7770 | 2.3885 | 0.4234 | 5.611 | 18.9982 |
| 1.4317 | 23.2 | 29000 | 1.7788 | 2.4084 | 0.4243 | 5.6463 | 18.9987 |
| 1.4269 | 24.0 | 30000 | 1.7786 | 2.428 | 0.4274 | 5.6495 | 18.9985 |
| 1.4135 | 24.8 | 31000 | 1.7883 | 2.3993 | 0.4244 | 5.6265 | 18.9981 |
| 1.4025 | 25.6 | 32000 | 1.7911 | 2.3972 | 0.4225 | 5.6432 | 18.9977 |
| 1.3874 | 26.4 | 33000 | 1.7930 | 2.3838 | 0.4207 | 5.6284 | 18.9989 |
| 1.4023 | 27.2 | 34000 | 1.7988 | 2.436 | 0.4277 | 5.6637 | 18.9981 |
| 1.3796 | 28.0 | 35000 | 1.8079 | 2.4162 | 0.4256 | 5.6432 | 18.9981 |
| 1.3729 | 28.8 | 36000 | 1.8124 | 2.3894 | 0.4225 | 5.6167 | 18.9975 |
| 1.3686 | 29.6 | 37000 | 1.8153 | 2.4301 | 0.4271 | 5.6606 | 18.9978 |
| 1.3603 | 30.4 | 38000 | 1.8174 | 2.4248 | 0.4253 | 5.6696 | 18.9973 |
| 1.3551 | 31.2 | 39000 | 1.8224 | 2.42 | 0.4243 | 5.67 | 18.9976 |
| 1.3504 | 32.0 | 40000 | 1.8246 | 2.4189 | 0.4254 | 5.6551 | 18.9977 |
| 1.3447 | 32.8 | 41000 | 1.8222 | 2.4234 | 0.425 | 5.6685 | 18.9969 |
| 1.3354 | 33.6 | 42000 | 1.8380 | 2.3975 | 0.422 | 5.6471 | 18.997 |
| 1.3304 | 34.4 | 43000 | 1.8416 | 2.4161 | 0.4247 | 5.6584 | 18.9975 |
| 1.3274 | 35.2 | 44000 | 1.8386 | 2.4271 | 0.4249 | 5.6804 | 18.9972 |
| 1.3238 | 36.0 | 45000 | 1.8361 | 2.4164 | 0.4241 | 5.6643 | 18.9972 |
| 1.3167 | 36.8 | 46000 | 1.8418 | 2.4359 | 0.426 | 5.6864 | 18.9973 |
| 1.3115 | 37.6 | 47000 | 1.8499 | 2.4068 | 0.4222 | 5.6682 | 18.9972 |
| 1.31 | 38.4 | 48000 | 1.8508 | 2.433 | 0.4256 | 5.6872 | 18.997 |
| 1.3085 | 39.2 | 49000 | 1.8477 | 2.4184 | 0.423 | 5.682 | 18.9967 |
| 1.3009 | 40.0 | 50000 | 1.8485 | 2.4182 | 0.4236 | 5.6753 | 18.9972 |
| 1.3028 | 40.8 | 51000 | 1.8547 | 2.4074 | 0.4222 | 5.6657 | 18.9977 |
| 1.2919 | 41.6 | 52000 | 1.8552 | 2.4199 | 0.4233 | 5.6825 | 18.9965 |
| 1.2945 | 42.4 | 53000 | 1.8652 | 2.4227 | 0.4238 | 5.6853 | 18.9975 |
| 1.2931 | 43.2 | 54000 | 1.8605 | 2.427 | 0.424 | 5.6907 | 18.9971 |
| 1.2838 | 44.0 | 55000 | 1.8647 | 2.4244 | 0.4244 | 5.6774 | 18.9972 |
| 1.2878 | 44.8 | 56000 | 1.8629 | 2.4209 | 0.4234 | 5.6848 | 18.997 |
| 1.2848 | 45.6 | 57000 | 1.8674 | 2.4291 | 0.4242 | 5.6931 | 18.9966 |
| 1.279 | 46.4 | 58000 | 1.8649 | 2.4253 | 0.4238 | 5.6898 | 18.9966 |
| 1.2862 | 47.2 | 59000 | 1.8643 | 2.4187 | 0.4228 | 5.686 | 18.9966 |
| 1.2798 | 48.0 | 60000 | 1.8643 | 2.4195 | 0.4231 | 5.6832 | 18.997 |
| 1.279 | 48.8 | 61000 | 1.8670 | 2.424 | 0.4239 | 5.6845 | 18.9966 |
| 1.2754 | 49.6 | 62000 | 1.8686 | 2.4234 | 0.4238 | 5.6842 | 18.9965 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
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