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

This model is a fine-tuned version of [lmqg/t5-small-squad-qg](https://huggingface.co/lmqg/t5-small-squad-qg) on the qg_squad dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5585
- Bleu: 0.1856
- Precisions: [0.4899881007730557, 0.23798056024064962, 0.14699694604682728, 0.09541131612394267]
- Brevity Penalty: 0.9231
- Length Ratio: 0.9259
- Translation Length: 126899
- Reference Length: 137056

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-----------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|
| 3.4717        | 1.0   | 1184  | 3.5703          | 0.1850 | [0.4884210026960997, 0.23740423378300554, 0.14702360671696277, 0.09591845720324058] | 0.9198          | 0.9228       | 126479             | 137056           |
| 3.4432        | 2.0   | 2368  | 3.5676          | 0.1847 | [0.4899809765377299, 0.23739313808702955, 0.14709099076226004, 0.09610180163262601] | 0.9173          | 0.9205       | 126160             | 137056           |
| 3.4207        | 3.0   | 3552  | 3.5654          | 0.1855 | [0.48690609948692964, 0.236654650074526, 0.14669770766719153, 0.09533838196460138]  | 0.9260          | 0.9286       | 127273             | 137056           |
| 3.4017        | 4.0   | 4736  | 3.5575          | 0.1861 | [0.4907433036243861, 0.23905491743183327, 0.14802083840498564, 0.09654473782730295] | 0.9195          | 0.9226       | 126449             | 137056           |
| 3.3862        | 5.0   | 5920  | 3.5540          | 0.1851 | [0.4916027385306181, 0.23877172085201795, 0.14769450336757936, 0.09608281170511601] | 0.9164          | 0.9197       | 126053             | 137056           |
| 3.3715        | 6.0   | 7104  | 3.5619          | 0.1847 | [0.4897172642552519, 0.23742624822429256, 0.14650127350144848, 0.09495653320731078] | 0.9209          | 0.9239       | 126620             | 137056           |
| 3.3602        | 7.0   | 8288  | 3.5581          | 0.1857 | [0.49199648336329865, 0.2390627732121, 0.14782006380301063, 0.09637410897534923]    | 0.9180          | 0.9212       | 126257             | 137056           |
| 3.3523        | 8.0   | 9472  | 3.5575          | 0.1856 | [0.4896288812767368, 0.23802266135985578, 0.14728396021137705, 0.09588544697859817] | 0.9215          | 0.9244       | 126698             | 137056           |
| 3.3439        | 9.0   | 10656 | 3.5582          | 0.1862 | [0.4919672196048933, 0.23971752696254087, 0.14848694668474074, 0.09658739962940087] | 0.9183          | 0.9215       | 126295             | 137056           |
| 3.3395        | 10.0  | 11840 | 3.5585          | 0.1856 | [0.4899881007730557, 0.23798056024064962, 0.14699694604682728, 0.09541131612394267] | 0.9231          | 0.9259       | 126899             | 137056           |


### 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_squad"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-squad-qg-a2c-spt-test", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "qg_squad", "type": "qg_squad", "config": "qg_squad", "split": "test", "args": "qg_squad"}, "metrics": [{"type": "bleu", "value": 0.1856298695745541, "name": "Bleu"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:qg_squad 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_squad #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

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


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


* Loss: 3.5585
* Bleu: 0.1856
* Precisions: [0.4899881007730557, 0.23798056024064962, 0.14699694604682728, 0.09541131612394267]
* Brevity Penalty: 0.9231
* Length Ratio: 0.9259
* Translation Length: 126899
* Reference Length: 137056


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_squad #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_squad #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