| --- |
| tags: |
| - generated_from_trainer |
| datasets: |
| - cnn_dailymail |
| model-index: |
| - name: roberta_gpt2_summarization_cnn_dailymail |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # roberta_gpt2_summarization_cnn_dailymail |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. |
| |
| ## Model description |
| This model uses RoBerta encoder and GPT2 decoder and fine-tuned on the summarization task. It got Rouge scores as follows: |
| |
| Rouge1= 35.886 |
| |
| Rouge2= 16.292 |
| |
| RougeL= 23.499 |
| ## Intended uses & limitations |
| To use its API: |
| |
| from transformers import RobertaTokenizerFast, GPT2Tokenizer, EncoderDecoderModel |
| |
| model = EncoderDecoderModel.from_pretrained("Ayham/roberta_gpt2_summarization_cnn_dailymail") |
|
|
| input_tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base') |
|
|
| output_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") |
|
|
| article = """Your Input Text""" |
|
|
| input_ids = input_tokenizer(article, return_tensors="pt").input_ids |
|
|
| output_ids = model.generate(input_ids) |
|
|
| print(output_tokenizer.decode(output_ids[0], skip_special_tokens=True)) |
|
|
| More information needed |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 2000 |
| - num_epochs: 3.0 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.12.0.dev0 |
| - Pytorch 1.10.0+cu111 |
| - Datasets 1.16.1 |
| - Tokenizers 0.10.3 |
| |
