readme: ---
  license: bigscience-bloom-rail-1.0
  tags:
  - generated_from_trainer
  model-index:
  - name: bloom-560m-finetuned-aeslc
    results: []
  widget:
  - text: "<s>Given the following email subject, generate a body for it:\nService Agreement\n\nBody:"
  
  inference:
    parameters:
      eos_token_id: 2
      max_length: 500
      
  ---
  
  <!-- 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. -->
  
  # bloom-560m-finetuned-aeslc
  
  This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.4199
  
  ## 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: 1
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 2.994         | 0.62  | 200  | 3.0855          |
  | 2.4454        | 1.23  | 400  | 3.0508          |
  | 2.3019        | 1.85  | 600  | 2.9731          |
  | 1.7647        | 2.46  | 800  | 3.1036          |
  | 1.636         | 3.08  | 1000 | 3.4199          |
  | 1.2469        | 3.69  | 1200 | 3.5381          |
  | 0.8443        | 4.31  | 1400 | 4.0697          |
  | 0.8214        | 4.92  | 1600 | 4.0181          |
  | 0.5355        | 5.54  | 1800 | 4.7636          |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  