

# 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

{"language": "ar", "tags": ["generated_from_trainer"], "datasets": ["LABR"], "widget": [{"text": "\u0643\u0627\u0646 \u0627\u0644\u0643\u0627\u062a\u0628 \u0645\u0645\u0643\u0646"}, {"text": "\u0643\u062a\u0627\u0628 \u0645\u0645\u062a\u0627\u0632 \u0648\u0644\u0643\u0646"}, {"text": "\u0631\u0648\u0627\u064a\u0629 \u062f\u0631\u0627\u0645\u064a\u0629 \u062c\u062f\u0627 \u0648\u0627\u0644\u0627\u0641\u0643\u0627\u0631 \u0628\u0633\u064a\u0637\u0629"}], "model-index": [{"name": "argpt2-goodreads", "results": []}]}
transformers pytorch gpt2 text-generation generated_from_trainer ar dataset:LABR autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #ar #dataset-LABR #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 



# 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



### 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

# 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 ### 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
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #ar #dataset-LABR #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # 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 ### 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
passage: TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #ar #dataset-LABR #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# 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### 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