
# QA2D-t5-small

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [QA2D](https://huggingface.co/datasets/domenicrosati/QA2D).
It achieves the following results on the evaluation set:
- Loss: 0.3236
- Rouge1: 89.8753
- Rouge2: 81.8104
- Rougel: 85.4253
- Rougelsum: 85.4236
- Bleu: 72.1080

See: [https://wandb.ai/domenicrosati/huggingface/runs/n1yallpe](https://wandb.ai/domenicrosati/huggingface/runs/n1yallpe) for training and eval stats and [https://github.com/domenicrosati/qa2d-models](https://github.com/domenicrosati/qa2d-models) for the code!

## Model description

A t5-model model to convert questions, answer pairs into statements.

Due to the way it's been trained the input should be all lower case and punctuation removed.
Use with `. ` as the seperator between question and answer.
> "where in the world is carmen. abruzzo"
> Output: "carmen is in abruzzo"

Thought punctation and upper case works.

```
from transformers import AutoTokenizer,  AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained('domenicrosati/QA2D-t5-small')
model = AutoModelForSeq2SeqLM.from_pretrained('domenicrosati/QA2D-t5-small')

question = "where in the world is carmen sandiego"
answer = "she is in abruzzo"
SEP = ". "

prompt = f'{question}{SEP}{answer}'
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
output_ids = model.generate(input_ids)
responses = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
# ['carmen sandiego is in abruzzo']
```

## Intended uses & limitations

To convert questions, answer pairs into statements.

## Training and evaluation data

Uses [QA2D](https://huggingface.co/datasets/domenicrosati/QA2D).

See [https://github.com/domenicrosati/qa2d-models](https://github.com/domenicrosati/qa2d-models)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Bleu    |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.3177        | 1.0   | 5060   | 0.3144          | 89.6379 | 81.3168 | 85.2036 | 85.1904   | 71.4255 |
| 0.2479        | 2.0   | 10120  | 0.3035          | 89.7816 | 81.6556 | 85.3541 | 85.3406   | 71.7248 |
| 0.2268        | 3.0   | 15180  | 0.3015          | 89.8287 | 81.698  | 85.3434 | 85.3387   | 71.8344 |
| 0.2111        | 4.0   | 20240  | 0.3014          | 89.8082 | 81.7192 | 85.4094 | 85.406    | 71.9172 |
| 0.1991        | 5.0   | 25300  | 0.3023          | 89.8776 | 81.7607 | 85.3912 | 85.3842   | 71.9417 |
| 0.1886        | 6.0   | 30360  | 0.3012          | 89.901  | 81.7614 | 85.3345 | 85.3315   | 72.0218 |
| 0.1803        | 7.0   | 35420  | 0.3010          | 89.8776 | 81.8189 | 85.4154 | 85.4097   | 72.0533 |
| 0.1724        | 8.0   | 40480  | 0.3041          | 89.9168 | 81.8663 | 85.4457 | 85.4447   | 72.1470 |
| 0.1654        | 9.0   | 45540  | 0.3076          | 89.8901 | 81.8536 | 85.4857 | 85.4863   | 72.0830 |
| 0.1601        | 10.0  | 50600  | 0.3083          | 89.9186 | 81.881  | 85.4653 | 85.4594   | 72.1048 |
| 0.1546        | 11.0  | 55660  | 0.3136          | 89.8958 | 81.8533 | 85.4217 | 85.4238   | 72.0752 |
| 0.1502        | 12.0  | 60720  | 0.3138          | 89.903  | 81.8604 | 85.4301 | 85.4267   | 72.1373 |
| 0.1461        | 13.0  | 65780  | 0.3140          | 89.8867 | 81.7945 | 85.3698 | 85.3662   | 72.0718 |
| 0.1423        | 14.0  | 70840  | 0.3171          | 89.8985 | 81.8221 | 85.4348 | 85.4331   | 72.1168 |
| 0.1392        | 15.0  | 75900  | 0.3186          | 89.8938 | 81.8246 | 85.402  | 85.3991   | 72.0858 |
| 0.1366        | 16.0  | 80960  | 0.3208          | 89.859  | 81.8133 | 85.4194 | 85.4182   | 72.1014 |
| 0.1344        | 17.0  | 86020  | 0.3222          | 89.8909 | 81.828  | 85.4392 | 85.435    | 72.1380 |
| 0.1324        | 18.0  | 91080  | 0.3226          | 89.8906 | 81.8351 | 85.4506 | 85.4441   | 72.1622 |
| 0.1309        | 19.0  | 96140  | 0.3231          | 89.8925 | 81.8369 | 85.4375 | 85.4366   | 72.1552 |
| 0.1305        | 20.0  | 101200 | 0.3236          | 89.8753 | 81.8104 | 85.4253 | 85.4236   | 72.1080 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["text2text-generation", "generated_from_trainer"], "datasets": ["domenicrosati/QA2D"], "metrics": ["rouge", "bleu"], "widget": [{"text": "where in the world is carmen sandiego. she is in abruzzo", "example_title": "Where is Carmen Sandiego?"}, {"text": "which province is halifax in. nova scotia", "example_title": "A Halifact"}], "model-index": [{"name": "QA2D-t5-small", "results": [{"task": {"type": "text2text-generation", "name": "Question to Declarative Sentence"}, "dataset": {"name": "domenicrosati/QA2D", "type": "domenicrosati/QA2D", "args": "plain_text"}, "metrics": [{"type": "rouge", "value": 89.8753, "name": "Rouge1"}, {"type": "rouge", "value": 81.8104, "name": "Rouge2"}, {"type": "rouge", "value": 85.4253, "name": "Rougel"}, {"type": "rouge", "value": 85.4236, "name": "Rougelsum"}, {"type": "bleu", "value": 72.108, "name": "Bleu"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:domenicrosati/QA2D license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-domenicrosati/QA2D #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

QA2D-t5-small
=============


This model is a fine-tuned version of t5-small on QA2D.
It achieves the following results on the evaluation set:


* Loss: 0.3236
* Rouge1: 89.8753
* Rouge2: 81.8104
* Rougel: 85.4253
* Rougelsum: 85.4236
* Bleu: 72.1080


See: URL for training and eval stats and URL for the code!


Model description
-----------------


A t5-model model to convert questions, answer pairs into statements.


Due to the way it's been trained the input should be all lower case and punctuation removed.
Use with '. ' as the seperator between question and answer.



> 
> "where in the world is carmen. abruzzo"
> Output: "carmen is in abruzzo"
> 
> 
> 


Thought punctation and upper case works.


Intended uses & limitations
---------------------------


To convert questions, answer pairs into statements.


Training and evaluation data
----------------------------


Uses QA2D.


See URL


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-domenicrosati/QA2D #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: 5.6e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-domenicrosati/QA2D #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: 5.6e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1