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# t5_small_cosmos_qa

This model is a fine-tuned version of [mamlong34/t5_small_race_mutlirc](https://huggingface.co/mamlong34/t5_small_race_mutlirc) on the cosmos_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5614
- Accuracy: 0.6067

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4811        | 1.0   | 3158 | 0.5445          | 0.5548   |
| 0.4428        | 2.0   | 6316 | 0.5302          | 0.5836   |
| 0.3805        | 3.0   | 9474 | 0.5614          | 0.6067   |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cosmos_qa"], "metrics": ["accuracy"], "model-index": [{"name": "t5_small_cosmos_qa", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer dataset:cosmos_qa license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cosmos_qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5\_small\_cosmos\_qa
=====================


This model is a fine-tuned version of mamlong34/t5\_small\_race\_mutlirc on the cosmos\_qa dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5614
* Accuracy: 0.6067


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.12.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.12.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cosmos_qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.12.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cosmos_qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 3.0### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.12.1
* Tokenizers 0.10.3