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# FLAN-T5_GLUE_finetuning_lr1e-3

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: 0.0998
- Accuracy: 0.871

## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1625        | 0.17  | 2500  | 0.1357          | 0.7747   |
| 0.1352        | 0.34  | 5000  | 0.1165          | 0.8158   |
| 0.1302        | 0.51  | 7500  | 0.1143          | 0.8193   |
| 0.1239        | 0.68  | 10000 | 0.1115          | 0.8279   |
| 0.1175        | 0.85  | 12500 | 0.1050          | 0.8343   |
| 0.1145        | 1.02  | 15000 | 0.1046          | 0.8339   |
| 0.1026        | 1.18  | 17500 | 0.0982          | 0.8435   |
| 0.1016        | 1.35  | 20000 | 0.0981          | 0.8465   |
| 0.1004        | 1.52  | 22500 | 0.0953          | 0.8495   |
| 0.0992        | 1.69  | 25000 | 0.0967          | 0.85     |
| 0.0984        | 1.86  | 27500 | 0.0926          | 0.8542   |
| 0.0944        | 2.03  | 30000 | 0.0951          | 0.8512   |
| 0.0813        | 2.2   | 32500 | 0.0933          | 0.8572   |
| 0.081         | 2.37  | 35000 | 0.0917          | 0.858    |
| 0.0806        | 2.54  | 37500 | 0.0949          | 0.8608   |
| 0.0805        | 2.71  | 40000 | 0.0901          | 0.8615   |
| 0.0786        | 2.88  | 42500 | 0.0889          | 0.8624   |
| 0.0733        | 3.05  | 45000 | 0.0974          | 0.8625   |
| 0.0599        | 3.22  | 47500 | 0.0942          | 0.8644   |
| 0.0596        | 3.38  | 50000 | 0.0950          | 0.8655   |
| 0.0595        | 3.55  | 52500 | 0.0921          | 0.866    |
| 0.0602        | 3.72  | 55000 | 0.0912          | 0.8678   |
| 0.0598        | 3.89  | 57500 | 0.0907          | 0.8684   |
| 0.053         | 4.06  | 60000 | 0.1013          | 0.8678   |
| 0.0421        | 4.23  | 62500 | 0.1010          | 0.8697   |
| 0.0426        | 4.4   | 65000 | 0.1025          | 0.8688   |
| 0.0427        | 4.57  | 67500 | 0.1023          | 0.8695   |
| 0.0415        | 4.74  | 70000 | 0.1004          | 0.8711   |
| 0.0411        | 4.91  | 72500 | 0.0998          | 0.871    |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0+cu117
- Datasets 2.10.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "FLAN-T5_GLUE_finetuning_lr1e-3", "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\_GLUE\_finetuning\_lr1e-3
=================================


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: 0.0998
* Accuracy: 0.871


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: 0.001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0+cu117
* Datasets 2.10.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0+cu117
* Datasets 2.10.1
* 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: 0.001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0+cu117
* Datasets 2.10.1
* 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: 0.001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0+cu117
* Datasets 2.10.1
* Tokenizers 0.12.1