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# t5-small-squad-qg-a2c-spt

This model is a fine-tuned version of [lmqg/t5-small-squad-qg](https://huggingface.co/lmqg/t5-small-squad-qg) on the qg_squadshifts dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4424
- Bleu: 0.2369
- Precisions: [0.5087032407189018, 0.27403783600926385, 0.18636099825885083, 0.1320389623167492]
- Brevity Penalty: 0.9790
- Length Ratio: 0.9793
- Translation Length: 42398
- Reference Length: 43296

## 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: 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: 10
- label_smoothing_factor: 0.15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Precisions                                                                          | Brevity Penalty | Length Ratio | Translation Length | Reference Length |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|
| 3.646         | 1.0   | 42   | 3.4501          | 0.2324 | [0.5031057356491574, 0.26807773815061764, 0.18061915046796256, 0.12696709585121602] | 0.9853          | 0.9854       | 42663              | 43296            |
| 3.5951        | 2.0   | 84   | 3.4456          | 0.2328 | [0.5061518076850274, 0.27000913957435696, 0.1832138903455107, 0.12926178476134007]  | 0.9759          | 0.9762       | 42264              | 43296            |
| 3.5572        | 3.0   | 126  | 3.4427          | 0.2355 | [0.505242954779515, 0.27049412978970455, 0.18334962341171734, 0.12953889087192133]  | 0.9867          | 0.9868       | 42724              | 43296            |
| 3.5295        | 4.0   | 168  | 3.4411          | 0.2351 | [0.5057055646865461, 0.27130317702804174, 0.1838566316518527, 0.12948538278525568]  | 0.9836          | 0.9837       | 42590              | 43296            |
| 3.4945        | 5.0   | 210  | 3.4418          | 0.2359 | [0.5068653913859875, 0.27228491562273527, 0.18446938010211442, 0.1297804417225878]  | 0.9839          | 0.9840       | 42605              | 43296            |
| 3.4771        | 6.0   | 252  | 3.4432          | 0.2375 | [0.507522591245159, 0.2735272802567554, 0.18594051980269422, 0.13157208938693074]   | 0.9839          | 0.9840       | 42605              | 43296            |
| 3.46          | 7.0   | 294  | 3.4431          | 0.2377 | [0.5092926294961487, 0.2746595987943041, 0.1869911632623497, 0.13212859294179272]   | 0.9803          | 0.9805       | 42453              | 43296            |
| 3.4656        | 8.0   | 336  | 3.4413          | 0.2368 | [0.5082384555547698, 0.2738076663025953, 0.18616789908655937, 0.1317669419321012]   | 0.9796          | 0.9798       | 42423              | 43296            |
| 3.443         | 9.0   | 378  | 3.4425          | 0.2373 | [0.5089378360532025, 0.27438532587485365, 0.18661869668658967, 0.13227530576778043] | 0.9792          | 0.9794       | 42404              | 43296            |
| 3.4455        | 10.0  | 420  | 3.4424          | 0.2369 | [0.5087032407189018, 0.27403783600926385, 0.18636099825885083, 0.1320389623167492]  | 0.9790          | 0.9793       | 42398              | 43296            |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.9.0
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["qg_squadshifts"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-squad-qg-a2c-spt", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "qg_squadshifts", "type": "qg_squadshifts", "config": "new_wiki", "split": "test", "args": "new_wiki"}, "metrics": [{"type": "bleu", "value": 0.23693792340861347, "name": "Bleu"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:qg_squadshifts license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-qg_squadshifts #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-squad-qg-a2c-spt
=========================


This model is a fine-tuned version of lmqg/t5-small-squad-qg on the qg\_squadshifts dataset.
It achieves the following results on the evaluation set:


* Loss: 3.4424
* Bleu: 0.2369
* Precisions: [0.5087032407189018, 0.27403783600926385, 0.18636099825885083, 0.1320389623167492]
* Brevity Penalty: 0.9790
* Length Ratio: 0.9793
* Translation Length: 42398
* Reference Length: 43296


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: 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: 10
* label\_smoothing\_factor: 0.15


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.9.0
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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: 10
* label\_smoothing\_factor: 0.15 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.9.0
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-qg_squadshifts #license-cc-by-4.0 #model-index #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: 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: 10
* label\_smoothing\_factor: 0.15 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.9.0
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-qg_squadshifts #license-cc-by-4.0 #model-index #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: 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: 10
* label\_smoothing\_factor: 0.15### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.9.0
* Datasets 2.9.0
* Tokenizers 0.13.2