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# google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b64

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: 1.8703
- Accuracy: 0.8691
- F1: 0.8686
- Bertscore F1: 0.9338
- Rouge1: 0.6063
- Rouge2: 0.3995
- Rougel: 0.5500
- Rougelsum: 0.5521
- Bleu: 0.4012

## 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: 64
- eval_batch_size: 64
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:|
| 1.4692        | 0.23  | 2000  | 1.7872          | 0.8212   | 0.8203 | 0.9287       | 0.5787 | 0.3685 | 0.5239 | 0.5257    | 0.3856 |
| 1.2505        | 0.47  | 4000  | 1.8808          | 0.8263   | 0.8264 | 0.9308       | 0.5870 | 0.3749 | 0.5321 | 0.5337    | 0.3904 |
| 1.2003        | 0.7   | 6000  | 1.8477          | 0.8475   | 0.8481 | 0.9325       | 0.5984 | 0.3913 | 0.5452 | 0.5469    | 0.4004 |
| 1.1624        | 0.93  | 8000  | 1.8244          | 0.8599   | 0.8587 | 0.9335       | 0.6029 | 0.3928 | 0.5441 | 0.5457    | 0.4024 |
| 1.1155        | 1.16  | 10000 | 1.8499          | 0.8695   | 0.8688 | 0.9331       | 0.6083 | 0.4019 | 0.5519 | 0.5540    | 0.4022 |
| 1.0913        | 1.4   | 12000 | 1.8703          | 0.8691   | 0.8686 | 0.9338       | 0.6063 | 0.3995 | 0.5500 | 0.5521    | 0.4012 |


### 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-b64", "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": 0.8691322901849218, "name": "Accuracy"}, {"type": "f1", "value": 0.8686267742768865, "name": "F1"}, {"type": "rouge", "value": 0.6062872493545299, "name": "Rouge1"}, {"type": "bleu", "value": 0.4012059786299585, "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-b64
===========================================================================


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: 1.8703
* Accuracy: 0.8691
* F1: 0.8686
* Bertscore F1: 0.9338
* Rouge1: 0.6063
* Rouge2: 0.3995
* Rougel: 0.5500
* Rougelsum: 0.5521
* Bleu: 0.4012


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: 64
* eval\_batch\_size: 64
* 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: 64
* eval\_batch\_size: 64
* 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: 64
* eval\_batch\_size: 64
* 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: 64
* eval\_batch\_size: 64
* 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