readme: ---
  license: bigscience-bloom-rail-1.0
  tags:
  - generated_from_trainer
  model-index:
  - name: bloom-560m-finetuned-the-stack-prolog
    results: []
  
  widget:
  - text: '% Define un hecho que indica que "hello" es un saludo
  saludo("hello").
  
  % Define una regla que indica que "world" es un objeto
  objeto("world").
  
  % Define una regla que combina el saludo y el objeto para producir la salida "Hola mundo"
  hola_mundo :-'
  ---
  
  <!-- 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-the-stack-prolog
  
  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: 0.2433
  
  ## 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: 2
  - 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: 2
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 1.2334        | 0.2   | 200  | 0.9993          |
  | 0.9174        | 0.4   | 400  | 0.7460          |
  | 0.7892        | 0.6   | 600  | 0.6046          |
  | 0.6805        | 0.8   | 800  | 0.4964          |
  | 0.5898        | 0.99  | 1000 | 0.4283          |
  | 0.411         | 1.19  | 1200 | 0.3721          |
  | 0.3705        | 1.39  | 1400 | 0.3182          |
  | 0.3516        | 1.59  | 1600 | 0.2795          |
  | 0.3298        | 1.79  | 1800 | 0.2528          |
  | 0.2721        | 1.99  | 2000 | 0.2433          |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.5.1
  - Tokenizers 0.13.0
  