readme: ---
  license: other
  tags:
  - generated_from_trainer
  datasets:
  - AlekseyKorshuk/amazon-reviews-input-output
  metrics:
  - accuracy
  model-index:
  - name: amazon-reviews-input-output-350m
    results:
    - task:
        name: Causal Language Modeling
        type: text-generation
      dataset:
        name: AlekseyKorshuk/amazon-reviews-input-output
        type: AlekseyKorshuk/amazon-reviews-input-output
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.0373780487804878
  ---
  
  <!-- 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. -->
  
  # amazon-reviews-input-output-350m
  
  This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the AlekseyKorshuk/amazon-reviews-input-output dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.0449
  - Accuracy: 0.0374
  - Samples: 100
  - Perplexity: 21.0084
  - Table: <wandb.data_types.Table object at 0x7f8a6005d580>
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - total_train_batch_size: 64
  - total_eval_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 3.2842        | 1.0   | 16   | 3.0449          | 0.0374   |
  
  
  ### Framework versions
  
  - Transformers 4.25.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  