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# le-fine-tune-mt5-small

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the ravkuk_summerize_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4588
- Rouge1: 0.16
- Rouge2: 0.0655
- Rougel: 0.1519
- Rougelsum: 0.1522
- Gen Len: 18.9886

## 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: 0.0014142135623730952
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 7.6563        | 1.0   | 197  | 3.0371          | 0.1002 | 0.028  | 0.0941 | 0.0941    | 18.8949 |
| 3.4565        | 2.0   | 394  | 2.8403          | 0.1204 | 0.0362 | 0.1154 | 0.1152    | 18.9744 |
| 3.0924        | 3.0   | 591  | 2.7173          | 0.1276 | 0.0431 | 0.1199 | 0.1202    | 18.9631 |
| 2.7357        | 4.0   | 788  | 2.5831          | 0.1494 | 0.0555 | 0.1415 | 0.1416    | 18.9744 |
| 2.4543        | 5.0   | 985  | 2.5135          | 0.1437 | 0.0545 | 0.1345 | 0.135     | 18.9886 |
| 2.2055        | 6.0   | 1182 | 2.5031          | 0.1544 | 0.0642 | 0.147  | 0.1472    | 18.9773 |
| 2.0147        | 7.0   | 1379 | 2.4554          | 0.158  | 0.0643 | 0.1484 | 0.1487    | 18.9688 |
| 1.8746        | 8.0   | 1576 | 2.4588          | 0.16   | 0.0655 | 0.1519 | 0.1522    | 18.9886 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["ravkuk_summerize_dataset"], "metrics": ["rouge"], "model-index": [{"name": "le-fine-tune-mt5-small", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "ravkuk_summerize_dataset", "type": "ravkuk_summerize_dataset", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "rouge", "value": 0.16, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard mt5 text2text-generation summarization generated_from_trainer dataset:ravkuk_summerize_dataset license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-ravkuk_summerize_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

le-fine-tune-mt5-small
======================


This model is a fine-tuned version of google/mt5-small on the ravkuk\_summerize\_dataset dataset.
It achieves the following results on the evaluation set:


* Loss: 2.4588
* Rouge1: 0.16
* Rouge2: 0.0655
* Rougel: 0.1519
* Rougelsum: 0.1522
* Gen Len: 18.9886


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: 0.0014142135623730952
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.3
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0014142135623730952
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.3
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-ravkuk_summerize_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0014142135623730952
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.3
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-ravkuk_summerize_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0014142135623730952
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.3
* num\_epochs: 8### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2