readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  datasets:
  - qg_squad
  metrics:
  - bleu
  model-index:
  - name: t5-small-squad-finetuned
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: qg_squad
        type: qg_squad
        config: qg_squad
        split: train
        args: qg_squad
      metrics:
      - name: Bleu
        type: bleu
        value: 0.18729932526273085
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # t5-small-squad-finetuned
  
  This model is a fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) on the qg_squad dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.8668
  - Bleu: 0.1873
  - Precisions: [0.5110525491352382, 0.245362761211552, 0.15215077757561193, 0.09884530767928974]
  - Brevity Penalty: 0.8988
  - Length Ratio: 0.9036
  - Translation Length: 108527
  - Reference Length: 120107
  
  ## 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: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Bleu   | Precisions                                                                          | Brevity Penalty | Length Ratio | Translation Length | Reference Length |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:-----------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|
  | 1.9131        | 1.0   | 2367  | 1.8960          | 0.1861 | [0.5106522785325583, 0.24527605096325475, 0.15213089101620028, 0.09826831888082575] | 0.8944          | 0.8996       | 108052             | 120107           |
  | 1.849         | 2.0   | 4734  | 1.8806          | 0.1849 | [0.5080246970549962, 0.24224047124755838, 0.1501267012945318, 0.09769844997651479]  | 0.8972          | 0.9021       | 108353             | 120107           |
  | 1.8168        | 3.0   | 7101  | 1.8727          | 0.1854 | [0.5098220476080425, 0.24339941601352388, 0.15093927730223472, 0.09804485712417446] | 0.8956          | 0.9007       | 108175             | 120107           |
  | 1.7923        | 4.0   | 9468  | 1.8700          | 0.1863 | [0.5133830790362698, 0.24615653748790878, 0.15267642711989654, 0.09868749835608512] | 0.8916          | 0.8971       | 107748             | 120107           |
  | 1.7748        | 5.0   | 11835 | 1.8689          | 0.1869 | [0.5141749342160318, 0.24699161674176884, 0.1534446643289472, 0.0998958319598096]   | 0.8898          | 0.8954       | 107549             | 120107           |
  | 1.7587        | 6.0   | 14202 | 1.8698          | 0.1864 | [0.5146328972484753, 0.24659953524399691, 0.1532201031824242, 0.09970271520116271]  | 0.8884          | 0.8942       | 107395             | 120107           |
  | 1.7468        | 7.0   | 16569 | 1.8680          | 0.1860 | [0.5112671501824734, 0.24460144371064352, 0.15145530742292898, 0.09809866056844169] | 0.8961          | 0.9012       | 108235             | 120107           |
  | 1.7378        | 8.0   | 18936 | 1.8670          | 0.1876 | [0.5122261914652045, 0.24610537728997678, 0.15275308797724588, 0.09927828458817849] | 0.8970          | 0.9020       | 108333             | 120107           |
  | 1.7312        | 9.0   | 21303 | 1.8676          | 0.1876 | [0.5117292997446746, 0.24587669400218548, 0.15249172858304044, 0.0989853996535511]  | 0.8984          | 0.9033       | 108489             | 120107           |
  | 1.7271        | 10.0  | 23670 | 1.8668          | 0.1873 | [0.5110525491352382, 0.245362761211552, 0.15215077757561193, 0.09884530767928974]   | 0.8988          | 0.9036       | 108527             | 120107           |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.2
  - Tokenizers 0.12.1
  