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# FLAN-T5_GLUE_finetuning_lr5e-4

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: 0.1003
- Accuracy: 0.8811

## 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: 0.0005
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1376        | 0.17  | 2500  | 0.1103          | 0.8277   |
| 0.1149        | 0.34  | 5000  | 0.1001          | 0.845    |
| 0.1085        | 0.51  | 7500  | 0.0953          | 0.8503   |
| 0.1055        | 0.68  | 10000 | 0.0942          | 0.8523   |
| 0.1011        | 0.85  | 12500 | 0.0922          | 0.8558   |
| 0.0983        | 1.02  | 15000 | 0.0923          | 0.8567   |
| 0.0831        | 1.18  | 17500 | 0.0886          | 0.863    |
| 0.0831        | 1.35  | 20000 | 0.0883          | 0.8637   |
| 0.0838        | 1.52  | 22500 | 0.0860          | 0.8667   |
| 0.0826        | 1.69  | 25000 | 0.0864          | 0.8666   |
| 0.0819        | 1.86  | 27500 | 0.0859          | 0.869    |
| 0.0782        | 2.03  | 30000 | 0.0880          | 0.8678   |
| 0.0623        | 2.2   | 32500 | 0.0867          | 0.8702   |
| 0.0632        | 2.37  | 35000 | 0.0870          | 0.8705   |
| 0.0627        | 2.54  | 37500 | 0.0878          | 0.8729   |
| 0.0637        | 2.71  | 40000 | 0.0841          | 0.8745   |
| 0.0627        | 2.88  | 42500 | 0.0841          | 0.8766   |
| 0.0579        | 3.05  | 45000 | 0.0925          | 0.8755   |
| 0.0456        | 3.22  | 47500 | 0.0915          | 0.8765   |
| 0.0456        | 3.38  | 50000 | 0.0909          | 0.8778   |
| 0.0452        | 3.55  | 52500 | 0.0890          | 0.8795   |
| 0.0462        | 3.72  | 55000 | 0.0873          | 0.8807   |
| 0.0453        | 3.89  | 57500 | 0.0892          | 0.8791   |
| 0.0401        | 4.06  | 60000 | 0.0988          | 0.8793   |
| 0.0319        | 4.23  | 62500 | 0.1013          | 0.8805   |
| 0.0323        | 4.4   | 65000 | 0.1017          | 0.8799   |
| 0.0323        | 4.57  | 67500 | 0.1012          | 0.8809   |
| 0.0321        | 4.74  | 70000 | 0.1011          | 0.8809   |
| 0.0315        | 4.91  | 72500 | 0.1003          | 0.8811   |


### 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_lr5e-4", "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\_lr5e-4
=================================


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: 0.1003
* Accuracy: 0.8811


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: 0.0005
* 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: 0.0005
* 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: 0.0005
* 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: 0.0005
* 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