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--- |
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language: en |
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tags: |
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- generated_from_trainer |
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datasets: |
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- allenai/mslr2022 |
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model-index: |
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- name: baseline |
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results: [] |
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--- |
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# Overview |
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This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the [Cochrane](https://github.com/allenai/mslr-shared-task#cochrane-dataset) dataset. The model received as input the titles and abstracts of up to 25 included studies for each example, concatenated by the `"</s>"` token. Global attention is applied to the special start token `"<s>"` and each of the document separator tokens `"</s>"`. The model slightly outperforms the reported results in the original paper: [MS2: Multi-Document Summarization of Medical Studies](https://arxiv.org/abs/2104.06486). See the [Cochrane leaderboard](https://leaderboard.allenai.org/mslr-cochrane/submissions/public) for results on the blind test set. |
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It achieves the following results on the `validation` set: |
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- Loss: 4.0216 |
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- Rouge1 Fmeasure Mean: 26.3026 |
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- Rouge2 Fmeasure Mean: 6.0324 |
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- Rougel Fmeasure Mean: 18.1513 |
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- Rougelsum Fmeasure Mean: 22.5031 |
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- Bertscore Hashcode: microsoft/deberta-xlarge-mnli_L40_no-idf_version=0.3.11(hug_trans=4.22.0.dev0)-rescaled_fast-tokenizer |
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- Bertscore F1 Mean: 20.5937 |
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- Seed: 42 |
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- Model Name Or Path: allenai/led-base-16384 |
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- Doc Sep Token: `"</s>"` |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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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: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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_ratio: 0.1 |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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- label_smoothing_factor: 0.1 |
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### Framework versions |
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- Transformers 4.22.0.dev0 |
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- Pytorch 1.12.0 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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