readme: ---
  license: apache-2.0
  tags:
  - text2text-generation
  - generated_from_trainer
  metrics:
  - rouge
  - bleu
  datasets:
  - domenicrosati/QA2D
  model-index:
  - name: QA2D-t5-base
    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: 90.1064
      - name: Rouge2
        type: rouge
        value: 82.378
      - name: Rougel
        type: rouge
        value: 85.7963
      - name: Rougelsum
        type: rouge
        value: 85.8004
      - name: Bleu
        type: bleu
        value: 72.7328
  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-base
  
  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on [QA2D](https://huggingface.co/datasets/domenicrosati/QA2D).
  It achieves the following results on the evaluation set:
  - Loss: 0.2563
  - Rouge1: 90.1064
  - Rouge2: 82.378
  - Rougel: 85.7963
  - Rougelsum: 85.8004
  - Bleu: 72.7328
  
  See: [https://wandb.ai/domenicrosati/huggingface/runs/nqf7gsws](https://wandb.ai/domenicrosati/huggingface/runs/nqf7gsws) 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-base')
  model = AutoModelForSeq2SeqLM.from_pretrained('domenicrosati/QA2D-t5-base')
  
  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']
  ```More information needed
  
  ## 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: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Bleu    |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 0.2304        | 1.0   | 5060  | 0.2512          | 90.044  | 82.2922 | 85.8021 | 85.8056   | 72.6252 |
  | 0.1746        | 2.0   | 10120 | 0.2525          | 90.097  | 82.3468 | 85.8191 | 85.8197   | 72.7480 |
  | 0.1512        | 3.0   | 15180 | 0.2563          | 90.1064 | 82.378  | 85.7963 | 85.8004   | 72.7328 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0a0+17540c5
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  