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# t5-end2end-question-generation

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3875
- Rouge1: 29.8409
- Rouge2: 15.2583
- Rougel: 25.4802
- Rougelsum: 28.8023
- Gen Len: 18.9971
- Bleu: 1.8149
- Bleu 0: 71.9158
- Bleu 1: 46.3975
- Bleu 2: 31.3479
- Bleu 3: 20.236

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len | Bleu   | Bleu 0  | Bleu 1  | Bleu 2  | Bleu 3  |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:------:|:-------:|:-------:|:-------:|:-------:|
| 1.4252        | 0.21  | 500   | 1.4638          | 29.5937 | 14.6438 | 25.1309 | 28.5076   | 18.9990 | 1.7595 | 70.9726 | 44.8789 | 29.8013 | 18.9402 |
| 1.3591        | 0.42  | 1000  | 1.4619          | 29.4017 | 14.7271 | 25.1139 | 28.3406   | 19.0    | 1.7286 | 70.9671 | 45.415  | 30.2413 | 19.1132 |
| 1.426         | 0.64  | 1500  | 1.4313          | 29.9163 | 15.0542 | 25.5098 | 28.852    | 19.0    | 1.8109 | 71.924  | 46.0312 | 30.8421 | 19.6842 |
| 1.5525        | 0.85  | 2000  | 1.4177          | 30.0353 | 15.2661 | 25.6495 | 28.9867   | 19.0    | 1.8387 | 72.1696 | 46.3888 | 31.1768 | 20.1203 |
| 1.5035        | 1.06  | 2500  | 1.4185          | 29.7649 | 15.1864 | 25.4353 | 28.738    | 19.0    | 1.7868 | 71.9618 | 46.6091 | 31.4797 | 20.209  |
| 1.4294        | 1.27  | 3000  | 1.4138          | 29.5473 | 14.877  | 25.1373 | 28.5195   | 18.9990 | 1.7516 | 71.3163 | 45.6707 | 30.7404 | 19.6335 |
| 1.4336        | 1.49  | 3500  | 1.4058          | 29.9003 | 15.213  | 25.4924 | 28.8375   | 19.0    | 1.799  | 71.8573 | 46.2609 | 31.2086 | 20.0675 |
| 1.4434        | 1.7   | 4000  | 1.3978          | 30.0046 | 15.2722 | 25.6091 | 28.9496   | 18.9990 | 1.839  | 72.2448 | 46.6283 | 31.463  | 20.2921 |
| 1.4285        | 1.91  | 4500  | 1.3984          | 30.0478 | 15.1083 | 25.4469 | 28.9337   | 18.9990 | 1.8247 | 71.6695 | 45.7508 | 30.7813 | 19.7828 |
| 1.3926        | 2.12  | 5000  | 1.3982          | 30.0837 | 15.4009 | 25.6203 | 29.0334   | 18.9990 | 1.8237 | 72.2626 | 46.662  | 31.5043 | 20.2789 |
| 1.369         | 2.33  | 5500  | 1.3980          | 29.9042 | 15.1828 | 25.4962 | 28.8323   | 18.9990 | 1.8064 | 71.8783 | 46.1411 | 31.0047 | 19.9691 |
| 1.3577        | 2.55  | 6000  | 1.3936          | 29.9335 | 15.2821 | 25.5855 | 28.9161   | 19.0    | 1.8099 | 71.8881 | 46.3101 | 31.3396 | 20.3185 |
| 1.3636        | 2.76  | 6500  | 1.3908          | 29.9512 | 15.2434 | 25.5476 | 28.9224   | 18.9995 | 1.8242 | 71.9772 | 46.3212 | 31.2688 | 20.1704 |
| 1.3799        | 2.97  | 7000  | 1.3900          | 29.9393 | 15.1658 | 25.4702 | 28.8729   | 18.9971 | 1.8055 | 71.9431 | 46.1286 | 30.9969 | 19.9389 |
| 1.3318        | 3.18  | 7500  | 1.3934          | 29.7982 | 15.132  | 25.3908 | 28.7333   | 18.9995 | 1.7908 | 71.7081 | 46.1832 | 31.1416 | 20.1409 |
| 1.3208        | 3.4   | 8000  | 1.3928          | 29.9378 | 15.1421 | 25.4586 | 28.8793   | 19.0    | 1.8258 | 71.7795 | 45.969  | 30.9173 | 19.9664 |
| 1.3135        | 3.61  | 8500  | 1.3888          | 29.9264 | 15.2179 | 25.5529 | 28.875    | 19.0    | 1.8363 | 71.9537 | 46.2706 | 31.2245 | 20.2624 |
| 1.323         | 3.82  | 9000  | 1.3868          | 29.8749 | 15.2251 | 25.4639 | 28.7949   | 18.9971 | 1.812  | 71.6918 | 46.1503 | 31.0437 | 19.9965 |
| 1.3325        | 4.03  | 9500  | 1.3868          | 29.8804 | 15.2658 | 25.4848 | 28.8238   | 18.9971 | 1.8105 | 71.9146 | 46.3617 | 31.2842 | 20.1447 |
| 1.296         | 4.24  | 10000 | 1.3882          | 29.941  | 15.28   | 25.5209 | 28.9109   | 18.9971 | 1.817  | 71.994  | 46.3801 | 31.216  | 20.0596 |
| 1.3027        | 4.46  | 10500 | 1.3883          | 29.8492 | 15.2017 | 25.4398 | 28.7911   | 18.9971 | 1.7994 | 71.8366 | 46.0939 | 30.9953 | 19.9115 |
| 1.3046        | 4.67  | 11000 | 1.3880          | 29.8538 | 15.2605 | 25.4897 | 28.8236   | 18.9971 | 1.8136 | 71.9285 | 46.3689 | 31.2969 | 20.1728 |
| 1.294         | 4.88  | 11500 | 1.3875          | 29.8409 | 15.2583 | 25.4802 | 28.8023   | 18.9971 | 1.8149 | 71.9158 | 46.3975 | 31.3479 | 20.236  |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge", "bleu"], "model-index": [{"name": "t5-end2end-question-generation", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-end2end-question-generation
==============================


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


* Loss: 1.3875
* Rouge1: 29.8409
* Rouge2: 15.2583
* Rougel: 25.4802
* Rougelsum: 28.8023
* Gen Len: 18.9971
* Bleu: 1.8149
* Bleu 0: 71.9158
* Bleu 1: 46.3975
* Bleu 2: 31.3479
* Bleu 3: 20.236


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2