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# flan-t5-base-extraction-cnndm_8000-all-loss-ep50

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: 1.7340
- Hint Hit Num: 2.3422
- Hint Precision: 0.4261
- Num: 5.4786
- Gen Len: 18.9996

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------------:|:------:|:-------:|
| 2.4434        | 0.75  | 100  | 1.9136          | 2.1212       | 0.4054         | 5.2222 | 18.9802 |
| 2.1367        | 1.49  | 200  | 1.8314          | 2.2656       | 0.4192         | 5.406  | 18.9969 |
| 2.0682        | 2.24  | 300  | 1.8232          | 2.2491       | 0.422          | 5.3241 | 18.9982 |
| 2.0331        | 2.99  | 400  | 1.7910          | 2.3106       | 0.4271         | 5.4092 | 18.999  |
| 1.9952        | 3.73  | 500  | 1.7794          | 2.2826       | 0.4217         | 5.4027 | 18.9992 |
| 1.9603        | 4.48  | 600  | 1.7714          | 2.3004       | 0.4245         | 5.4066 | 18.9994 |
| 1.9473        | 5.22  | 700  | 1.7633          | 2.3693       | 0.4321         | 5.4749 | 18.9996 |
| 1.9293        | 5.97  | 800  | 1.7619          | 2.2834       | 0.4222         | 5.3942 | 18.9997 |
| 1.9092        | 6.72  | 900  | 1.7556          | 2.3342       | 0.4263         | 5.4603 | 18.9999 |
| 1.8954        | 7.46  | 1000 | 1.7560          | 2.3196       | 0.4262         | 5.4271 | 18.9994 |
| 1.886         | 8.21  | 1100 | 1.7389          | 2.3588       | 0.4307         | 5.4604 | 18.9996 |
| 1.8532        | 8.96  | 1200 | 1.7499          | 2.3351       | 0.4279         | 5.4403 | 18.9998 |
| 1.8496        | 9.7   | 1300 | 1.7390          | 2.3407       | 0.4267         | 5.4666 | 18.9996 |
| 1.8309        | 10.45 | 1400 | 1.7436          | 2.337        | 0.4281         | 5.4377 | 18.9992 |
| 1.8208        | 11.19 | 1500 | 1.7375          | 2.3605       | 0.4292         | 5.4822 | 18.9997 |
| 1.8082        | 11.94 | 1600 | 1.7390          | 2.3582       | 0.4303         | 5.46   | 18.9993 |
| 1.805         | 12.69 | 1700 | 1.7340          | 2.3422       | 0.4261         | 5.4786 | 18.9996 |
| 1.7704        | 13.43 | 1800 | 1.7448          | 2.351        | 0.428          | 5.4723 | 18.9996 |
| 1.7868        | 14.18 | 1900 | 1.7367          | 2.3787       | 0.4303         | 5.5085 | 18.9996 |
| 1.7693        | 14.93 | 2000 | 1.7445          | 2.3526       | 0.4292         | 5.4647 | 18.9992 |
| 1.7541        | 15.67 | 2100 | 1.7391          | 2.3799       | 0.4314         | 5.5012 | 18.9996 |
| 1.7465        | 16.42 | 2200 | 1.7475          | 2.3556       | 0.4281         | 5.4835 | 18.9991 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "flan-t5-base-extraction-cnndm_8000-all-loss-ep50", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

flan-t5-base-extraction-cnndm\_8000-all-loss-ep50
=================================================


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


* Loss: 1.7340
* Hint Hit Num: 2.3422
* Hint Precision: 0.4261
* Num: 5.4786
* Gen Len: 18.9996


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



### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.5.1
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.5.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #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: 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 ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.5.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #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: 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### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.5.1
* Tokenizers 0.12.1