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# t5-base-axriv-to-abstract-3

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the arxiv-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6588
- Rouge1: 0.1301
- Rouge2: 0.0481
- Rougel: 0.1047
- Rougelsum: 0.1047
- Gen Len: 19.0

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.5634        | 0.61  | 4000  | 2.4010          | 0.1339 | 0.0519 | 0.1074 | 0.1075    | 19.0    |
| 2.4533        | 1.21  | 8000  | 2.3582          | 0.1318 | 0.0517 | 0.1067 | 0.1067    | 19.0    |
| 3.0109        | 1.82  | 12000 | 2.7488          | 0.1366 | 0.0509 | 0.1096 | 0.1095    | 18.9963 |
| 2.9063        | 2.42  | 16000 | 2.6588          | 0.1301 | 0.0481 | 0.1047 | 0.1047    | 19.0    |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["arxiv-summarization"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-axriv-to-abstract-3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "arxiv-summarization", "type": "arxiv-summarization", "config": "section", "split": "validation", "args": "section"}, "metrics": [{"type": "rouge", "value": 0.1301, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:arxiv-summarization license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-arxiv-summarization #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

t5-base-axriv-to-abstract-3
===========================


This model is a fine-tuned version of t5-base on the arxiv-summarization dataset.
It achieves the following results on the evaluation set:


* Loss: 2.6588
* Rouge1: 0.1301
* Rouge2: 0.0481
* Rougel: 0.1047
* Rougelsum: 0.1047
* Gen Len: 19.0


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: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-arxiv-summarization #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-arxiv-summarization #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3