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# t5-small-finetuned-contradiction

This model is a fine-tuned version of [domenicrosati/t5-small-finetuned-contradiction](https://huggingface.co/domenicrosati/t5-small-finetuned-contradiction) on the snli dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0458
- Rouge1: 34.4237
- Rouge2: 14.5442
- Rougel: 32.5483
- Rougelsum: 32.5785

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.8605        | 1.0   | 2863  | 2.0813          | 34.4597 | 14.5186 | 32.6909 | 32.7097   |
| 1.9209        | 2.0   | 5726  | 2.0721          | 34.3859 | 14.5733 | 32.5188 | 32.5524   |
| 1.9367        | 3.0   | 8589  | 2.0623          | 34.4192 | 14.455  | 32.581  | 32.5962   |
| 1.9539        | 4.0   | 11452 | 2.0565          | 34.5148 | 14.6131 | 32.6786 | 32.7174   |
| 1.9655        | 5.0   | 14315 | 2.0538          | 34.4393 | 14.6439 | 32.6344 | 32.6587   |
| 1.9683        | 6.0   | 17178 | 2.0493          | 34.7199 | 14.7763 | 32.8625 | 32.8782   |
| 1.9735        | 7.0   | 20041 | 2.0476          | 34.5366 | 14.6362 | 32.6939 | 32.7177   |
| 1.98          | 8.0   | 22904 | 2.0458          | 34.5    | 14.5695 | 32.6219 | 32.6478   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["snli"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-contradiction", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "snli", "type": "snli", "args": "plain_text"}, "metrics": [{"type": "rouge", "value": 34.4237, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation summarization generated_from_trainer dataset:snli license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-snli #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-finetuned-contradiction
================================


This model is a fine-tuned version of domenicrosati/t5-small-finetuned-contradiction on the snli dataset.
It achieves the following results on the evaluation set:


* Loss: 2.0458
* Rouge1: 34.4237
* Rouge2: 14.5442
* Rougel: 32.5483
* Rougelsum: 32.5785


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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu102
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu102
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-snli #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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu102
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-snli #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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu102
* Datasets 2.1.0
* Tokenizers 0.12.1