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