argpt2-goodreads

argpt2-goodreads

This model is a fine-tuned version of 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