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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- seq2seq |
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- summarization |
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datasets: |
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- samsum |
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metrics: |
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- rouge |
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widget: |
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- text: | |
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Emily: Hey Alex, have you heard about the new restaurant that opened |
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downtown? |
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Alex: No, I haven't. What's it called? |
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Emily: It's called "Savory Bites." They say it has the best pasta in town. |
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Alex: That sounds delicious. When are you thinking of checking it out? |
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Emily: How about this Saturday? We can make it a dinner date. |
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Alex: Sounds like a plan, Emily. I'm looking forward to it. |
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model-index: |
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- name: bart-large-xsum-samsum |
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results: |
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- task: |
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type: summarization |
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name: Summarization |
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dataset: |
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name: >- |
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SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive |
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Summarization |
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type: samsum |
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metrics: |
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- type: rouge-1 |
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value: 54.3073 |
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name: Validation ROUGE-1 |
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- type: rouge-2 |
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value: 29.0947 |
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name: Validation ROUGE-2 |
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- type: rouge-l |
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value: 44.4676 |
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name: Validation ROUGE-L |
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--- |
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# bart-large-xsum-samsum |
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This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the [samsum dataset](https://huggingface.co/datasets/samsum). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.759 |
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- Rouge1: 54.3073 |
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- Rouge2: 29.0947 |
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- Rougel: 44.4676 |
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- Rougelsum: 49.895 |
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## Model description |
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This model tends to generate less verbose summaries compared to [AdamCodd/bart-large-cnn-samsum](https://huggingface.co/AdamCodd/bart-large-cnn-samsum), yet I find its quality to be superior (which is reflected in the metrics). |
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## Intended uses & limitations |
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Suitable for summarizing dialogue-style text, it may not perform as well with other types of text formats. |
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```python |
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from transformers import pipeline |
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summarizer = pipeline("summarization", model="AdamCodd/bart-large-xsum-samsum") |
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conversation = '''Emily: Hey Alex, have you heard about the new restaurant that opened downtown? |
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Alex: No, I haven't. What's it called? |
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Emily: It's called "Savory Bites." They say it has the best pasta in town. |
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Alex: That sounds delicious. When are you thinking of checking it out? |
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Emily: How about this Saturday? We can make it a dinner date. |
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Alex: Sounds like a plan, Emily. I'm looking forward to it. |
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''' |
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result = summarizer(conversation) |
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print(result) |
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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: 3e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 1270 |
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- optimizer: AdamW 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: 150 |
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- num_epochs: 1 |
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### Training results |
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| key | value | |
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| --- | ----- | |
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| eval_rouge1 | 54.3073 | |
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| eval_rouge2 | 29.0947 | |
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| eval_rougeL | 44.4676 | |
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| eval_rougeLsum | 49.895 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Accelerate 0.24.1 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.3 |
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If you want to support me, you can [here](https://ko-fi.com/adamcodd). |
