
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b48

This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the esnli dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8720
- Accuracy: 0.8622
- F1: 0.8623
- Bertscore F1: 0.9329
- Rouge1: 0.6059
- Rouge2: 0.3988
- Rougel: 0.5475
- Rougelsum: 0.5496
- Bleu: 0.4047

## 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: 0.001
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Bertscore F1 | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:|
| 1.5084        | 0.17  | 2000  | 1.7484          | 0.8001   | 0.7997 | 0.9271       | 0.5768 | 0.3695 | 0.5209 | 0.5229    | 0.3703 |
| 1.2745        | 0.35  | 4000  | 1.8137          | 0.8113   | 0.8110 | 0.9304       | 0.5881 | 0.3804 | 0.5305 | 0.5325    | 0.3853 |
| 1.2287        | 0.52  | 6000  | 1.8358          | 0.8392   | 0.8403 | 0.9298       | 0.5828 | 0.3747 | 0.5282 | 0.5301    | 0.3778 |
| 1.1964        | 0.7   | 8000  | 1.8432          | 0.8430   | 0.8437 | 0.9326       | 0.5974 | 0.3905 | 0.5447 | 0.5462    | 0.3998 |
| 1.1674        | 0.87  | 10000 | 1.8567          | 0.8507   | 0.8485 | 0.9310       | 0.5947 | 0.3888 | 0.5383 | 0.5402    | 0.3892 |
| 1.1371        | 1.05  | 12000 | 1.8720          | 0.8622   | 0.8623 | 0.9329       | 0.6059 | 0.3988 | 0.5475 | 0.5496    | 0.4047 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["esnli"], "metrics": ["accuracy", "f1", "rouge", "bleu"], "model-index": [{"name": "google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b48", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "esnli", "type": "esnli", "config": "plain_text", "split": "validation", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.8622231253810201, "name": "Accuracy"}, {"type": "f1", "value": 0.8623314280769628, "name": "F1"}, {"type": "rouge", "value": 0.605873896307076, "name": "Rouge1"}, {"type": "bleu", "value": 0.40472213589689604, "name": "Bleu"}]}]}]}
transformers pytorch t5 text2text-generation generated_from_trainer dataset:esnli license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-esnli #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

google-flan-t5-small-e-snli-generation-label\_and\_explanation-selected-b48
===========================================================================


This model is a fine-tuned version of google/flan-t5-small on the esnli dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8720
* Accuracy: 0.8622
* F1: 0.8623
* Bertscore F1: 0.9329
* Rouge1: 0.6059
* Rouge2: 0.3988
* Rougel: 0.5475
* Rougelsum: 0.5496
* Bleu: 0.4047


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: 0.001
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-esnli #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: 0.001
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-esnli #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: 0.001
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2