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--- |
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library_name: transformers |
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base_model: google/pegasus-cnn_dailymail |
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tags: |
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- generated_from_trainer |
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datasets: |
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- samsum |
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model-index: |
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- name: pegasus-finetuned-samsum |
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results: |
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- task: |
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type: summarization |
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dataset: |
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name: samsum |
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type: samsum |
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metrics: |
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- name: ROUGE-1 |
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type: ROUGE-1 |
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value: 0.44030729209257835 |
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- name: ROUGE-2 |
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type: ROUGE-2 |
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value: 0.21015363722992564 |
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- name: ROUGE-L |
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type: ROUGE-L |
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value: 0.3491351151672408 |
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- name: ROUGE-Lsum |
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type: ROUGE-Lsum |
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value: 0.3492708872856547 |
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language: |
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- en |
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pipeline_tag: summarization |
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metrics: |
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- rouge |
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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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# pegasus-finetuned-samsum |
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This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4245 |
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- Rouge-1: 0.4403 |
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- Rouge-2: 0.2102 |
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- Rouge-L: 0.3491 |
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- Rouge-Lsum: 0.3493 |
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## Model description |
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This would be the 10th best-performing model on the [Papers with Code SAMsum leaderboard](https://paperswithcode.com/sota/text-summarization-on-samsum-corpus), |
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as of April 8, 2025. |
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## Intended uses & limitations |
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This model is designed for abstractive dialogue summarization. It can take in multi-turn conversations and generate concise summaries. |
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## How to Use |
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```Python |
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from transformers import pipeline |
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# Load summarization pipeline |
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model_name = "avanishd/pegasus-finetuned-samsum/" |
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summarizer = pipeline("summarization", model=model_name, tokenizer=model_name) |
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# Sample conversation |
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dialogue = """ |
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John: Hey, are you free tomorrow? |
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Alice: I think so, why? |
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John: Want to catch a movie or grab lunch? |
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Alice: Sure, lunch sounds good. What time? |
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John: Let's say 1 PM at the new place downtown? |
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Alice: Works for me! |
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""" |
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# Generate summary |
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summary = summarizer(dialogue, max_length=60, min_length=15, do_sample=False)[0]['summary_text'] |
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print("Summary:", summary) |
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``` |
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## Training and evaluation data |
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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: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 16 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.5798 | 0.9992 | 920 | 1.4245 | |
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### Framework versions |
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- Transformers 4.50.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
