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