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# mt5-summarize-sum-test-internal

This model is a fine-tuned version of [raquelclemente/mt5-summarize-sum](https://huggingface.co/raquelclemente/mt5-summarize-sum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0371
- Rouge1: 0.4910
- Rouge2: 0.3100
- Rougel: 0.3734
- Rougelsum: 0.3734

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 19
- num_epochs: 19

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log        | 1.9   | 5    | 1.0002          | 0.4672 | 0.3016 | 0.3936 | 0.3936    |
| 0.9822        | 3.81  | 10   | 0.9942          | 0.3989 | 0.3127 | 0.3713 | 0.3713    |
| 0.9822        | 5.71  | 15   | 1.0433          | 0.5093 | 0.3425 | 0.4298 | 0.4298    |
| 0.8459        | 7.62  | 20   | 1.0712          | 0.5394 | 0.3463 | 0.4320 | 0.4320    |
| 0.8459        | 9.52  | 25   | 1.0358          | 0.4281 | 0.2476 | 0.3160 | 0.3160    |
| 0.642         | 11.43 | 30   | 1.0335          | 0.4439 | 0.2582 | 0.3224 | 0.3224    |
| 0.642         | 13.33 | 35   | 1.0371          | 0.4910 | 0.3100 | 0.3734 | 0.3734    |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-summarize-sum-test-internal", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-summarize-sum-test-internal
===============================


This model is a fine-tuned version of raquelclemente/mt5-summarize-sum on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0371
* Rouge1: 0.4910
* Rouge2: 0.3100
* Rougel: 0.3734
* Rougelsum: 0.3734


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: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 19
* num\_epochs: 19


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 19
* num\_epochs: 19 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #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: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 19
* num\_epochs: 19 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2