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# mt5-small-finetuned-arxiv-summarization

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the pubmed-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.6353
- Rouge2: 0.0849
- Rougel: 0.5942
- Rougelsum: 0.6117

## 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: 5.6e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.0           | 1.0   | 1999 | nan             | 0.6353 | 0.0849 | 0.5942 | 0.6117    |
| 0.0           | 2.0   | 3998 | nan             | 0.6353 | 0.0849 | 0.5942 | 0.6117    |
| 0.0           | 3.0   | 5997 | nan             | 0.6353 | 0.0849 | 0.5942 | 0.6117    |
| 0.0           | 4.0   | 7996 | nan             | 0.6353 | 0.0849 | 0.5942 | 0.6117    |
| 0.0           | 5.0   | 9995 | nan             | 0.6353 | 0.0849 | 0.5942 | 0.6117    |


### 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": ["pubmed-summarization"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-arxiv-summarization", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pubmed-summarization", "type": "pubmed-summarization", "config": "section", "split": "validation", "args": "section"}, "metrics": [{"type": "rouge", "value": 0.6353, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard mt5 text2text-generation summarization generated_from_trainer dataset:pubmed-summarization 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-pubmed-summarization #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-small-finetuned-arxiv-summarization
=======================================


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


* Loss: nan
* Rouge1: 0.6353
* Rouge2: 0.0849
* Rougel: 0.5942
* Rougelsum: 0.6117


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: 5.6e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP


### 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: 5.6e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### 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-pubmed-summarization #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: 5.6e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### 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-pubmed-summarization #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: 5.6e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


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