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# t5_recommendation_piscine_equipements_large

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: 0.8443
- Rouge1: 34.1161
- Rouge2: 26.3432
- Rougel: 33.9077
- Rougelsum: 33.9241
- Gen Len: 18.7083

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 0.92  | 3    | 2.3712          | 0.0     | 0.0     | 0.0     | 0.0       | 0.0     |
| No log        | 1.92  | 6    | 1.2669          | 22.7772 | 20.7387 | 22.8081 | 22.7953   | 9.0958  |
| No log        | 2.92  | 9    | 0.9796          | 31.9168 | 24.7944 | 31.8451 | 31.7950   | 18.8379 |
| No log        | 3.92  | 12   | 0.9213          | 29.0572 | 22.0777 | 29.0892 | 29.0306   | 18.5734 |
| No log        | 4.92  | 15   | 0.8704          | 23.9262 | 16.1217 | 23.7935 | 23.7483   | 17.7846 |
| No log        | 5.92  | 18   | 0.8323          | 28.4808 | 25.0648 | 28.4294 | 28.4832   | 12.6973 |
| No log        | 6.92  | 21   | 0.8144          | 28.3291 | 20.5699 | 28.0259 | 27.9503   | 18.1416 |
| No log        | 7.92  | 24   | 0.8465          | 24.8294 | 21.5138 | 24.6756 | 24.6961   | 9.9852  |
| No log        | 8.92  | 27   | 0.8597          | 29.5795 | 23.0855 | 29.4314 | 29.4205   | 17.4714 |
| No log        | 9.92  | 30   | 0.8443          | 34.1161 | 26.3432 | 33.9077 | 33.9241   | 18.7083 |


### Framework versions

- Transformers 4.26.0
- Pytorch 2.0.0+cpu
- Datasets 2.8.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5_recommendation_piscine_equipements_large", "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 

t5\_recommendation\_piscine\_equipements\_large
===============================================


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


* Loss: 0.8443
* Rouge1: 34.1161
* Rouge2: 26.3432
* Rougel: 33.9077
* Rougelsum: 33.9241
* Gen Len: 18.7083


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 2.0.0+cpu
* Datasets 2.8.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 2.0.0+cpu
* Datasets 2.8.0
* Tokenizers 0.13.3
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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 2.0.0+cpu
* Datasets 2.8.0
* Tokenizers 0.13.3
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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 2.0.0+cpu
* Datasets 2.8.0
* Tokenizers 0.13.3