---
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