readme: ---
  tags:
  - generated_from_trainer
  language: ar
  datasets:
   - LABR
  widget:
   - text: "كان الكاتب ممكن"
   - text: "كتاب ممتاز ولكن"
   - text: "رواية درامية جدا والافكار بسيطة"
  model-index:
  - name: argpt2-goodreads
    results: []
  ---
  
  
  # argpt2-goodreads
  
  This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an goodreads LABR  dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.4389
  
  ## Model description
  
  Generate sentences either positive/negative examples based on goodreads corpus in arabic language.
  
  ## Intended uses & limitations
  
  the model fine-tuned on arabic language only with aspect to generate sentences such as reviews in order todo the same for other languages you need to fine-tune it in your own.
  any harmful content generated by GPT2 should not be used in anywhere.
  
  ## Training and evaluation data
  training and validation done on goodreads dataset LABR 80% for trainng and 20% for testing
  ## Usage
  
  ```
  from transformers import AutoTokenizer, AutoModelForCausalLM
  
  tokenizer = AutoTokenizer.from_pretrained("mofawzy/argpt2-goodreads")
  
  model = AutoModelForCausalLM.from_pretrained("mofawzy/argpt2-goodreads")
  
  ```
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - distributed_type: tpu
  - num_devices: 8
  - total_train_batch_size: 128
  - total_eval_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20.0
  
  ### Training results
   - train_loss = 1.474
  
  ### Evaluation results
   - eval_loss = 1.4389
  
  
  ### train metrics
    - epoch                    =       20.0
    - train_loss               =      1.474
    - train_runtime            = 2:18:14.51
    - train_samples            =     108110
    - train_samples_per_second =    260.678
    - train_steps_per_second   =      2.037
  
  
  
  ### eval metrics
    - epoch                   =       20.0
    - eval_loss               =     1.4389
    - eval_runtime            = 0:04:37.01
    - eval_samples            =      27329
    - eval_samples_per_second =     98.655
    - eval_steps_per_second   =      0.773
    - perplexity              =     4.2162
  
  
  
  
  ### Framework versions
  
  - Transformers 4.13.0.dev0
  - Pytorch 1.10.0+cu102
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  