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# t5-large-slots

This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0889
- Acc: 0.76
- True Num: 11167
- Num: 14748

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Acc  | True Num | Num   |
|:-------------:|:-----:|:-----:|:---------------:|:----:|:--------:|:-----:|
| 0.3539        | 0.56  | 1000  | 0.2669          | 0.56 | 8264     | 14748 |
| 0.2523        | 1.13  | 2000  | 0.2031          | 0.56 | 8317     | 14748 |
| 0.2003        | 1.69  | 3000  | 0.1498          | 0.58 | 8496     | 14748 |
| 0.1609        | 2.25  | 4000  | 0.1284          | 0.58 | 8612     | 14748 |
| 0.1431        | 2.82  | 5000  | 0.1119          | 0.59 | 8675     | 14748 |
| 0.1236        | 3.38  | 6000  | 0.1054          | 0.59 | 8737     | 14748 |
| 0.1172        | 3.95  | 7000  | 0.0981          | 0.59 | 8773     | 14748 |
| 0.1027        | 4.51  | 8000  | 0.0955          | 0.6  | 8787     | 14748 |
| 0.0968        | 5.07  | 9000  | 0.0931          | 0.6  | 8807     | 14748 |
| 0.0911        | 5.64  | 10000 | 0.0895          | 0.6  | 8787     | 14748 |
| 0.0852        | 6.2   | 11000 | 0.0912          | 0.6  | 8840     | 14748 |
| 0.0823        | 6.76  | 12000 | 0.0880          | 0.6  | 8846     | 14748 |
| 0.0768        | 7.33  | 13000 | 0.0915          | 0.6  | 8879     | 14748 |
| 0.0758        | 7.89  | 14000 | 0.0892          | 0.6  | 8853     | 14748 |
| 0.0708        | 8.46  | 15000 | 0.0885          | 0.6  | 8884     | 14748 |
| 0.0701        | 9.02  | 16000 | 0.0884          | 0.6  | 8915     | 14748 |
| 0.0685        | 9.58  | 17000 | 0.0884          | 0.6  | 8921     | 14748 |


### Framework versions

- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-large-slots", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-large-slots
==============


This model is a fine-tuned version of t5-large on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0889
* Acc: 0.76
* True Num: 11167
* Num: 14748


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: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0


### Training results



### Framework versions


* Transformers 4.12.5
* Pytorch 1.10.0+cu102
* Datasets 1.15.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.12.5
* Pytorch 1.10.0+cu102
* Datasets 1.15.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.12.5
* Pytorch 1.10.0+cu102
* Datasets 1.15.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0### Training results### Framework versions


* Transformers 4.12.5
* Pytorch 1.10.0+cu102
* Datasets 1.15.1
* Tokenizers 0.10.3