readme: ---
  license: apache-2.0
  tags:
  - text2text-generation
  - generated_from_trainer
  metrics:
  - rouge
  - bleu
  datasets:
  - domenicrosati/QA2D
  model-index:
  - name: QA2D-t5-small
    results:
    - task:
        name: Question to Declarative Sentence
        type: text2text-generation
      dataset:
        name: domenicrosati/QA2D
        type: domenicrosati/QA2D
        args: plain_text
      metrics:
      - name: Rouge1
        type: rouge
        value: 89.8753
      - name: Rouge2
        type: rouge
        value: 81.8104
      - name: Rougel
        type: rouge
        value: 85.4253
      - name: Rougelsum
        type: rouge
        value: 85.4236
      - name: Bleu
        type: bleu
        value: 72.1080
  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"
  ---
  
  # 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 | 8