readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - gsm8k
  model-index:
  - name: flan-t5-base-finetuned-gsm8k
    results: []
  widget:
  - text: "Please, answer the following question reasoning step-by-step:
  Manu bought 4 apples and lost one in the market. How many apples does Manu have?"
  ---
  
  <!-- 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. -->
  
  # flan-t5-base-finetuned-gsm8k
  
  This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the gsm8k dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3652
  - Rouge2 Precision: 0.3914
  - Rouge2 Recall: 0.0816
  - Rouge2 Fmeasure: 0.1308
  
  ## 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: 4
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
  |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
  | 0.425         | 1.0   | 1869 | 0.3942          | 0.3707           | 0.0774        | 0.1238          |
  | 0.3849        | 2.0   | 3738 | 0.3769          | 0.3809           | 0.0795        | 0.1272          |
  | 0.3663        | 3.0   | 5607 | 0.3698          | 0.3808           | 0.0805        | 0.1285          |
  | 0.3553        | 4.0   | 7476 | 0.3659          | 0.3863           | 0.0805        | 0.129           |
  | 0.3421        | 5.0   | 9345 | 0.3652          | 0.3914           | 0.0816        | 0.1308          |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  