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--- |
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language: |
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- en |
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tags: |
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- summarization |
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datasets: |
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- scientific_papers |
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metrics: |
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- rouge |
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model-index: |
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- name: ccdv/lsg-bart-base-16384-pubmed |
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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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**This model relies on a custom modeling file, you need to add trust_remote_code=True**\ |
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**See [\#13467](https://github.com/huggingface/transformers/pull/13467)** |
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# ccdv/lsg-bart-base-16384-pubmed |
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This model is a fine-tuned version of [ccdv/lsg-bart-base-4096-pubmed](https://huggingface.co/ccdv/lsg-bart-base-4096-pubmed) on the scientific_papers pubmed dataset. \ |
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It achieves the following results on the test set: |
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| Length | Global tokens | Sparse Type | Block Size | Sparsity | Connexions | R1 | R2 | RL | RLsum | |
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|:------ |:------------- |:----------- |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- | |
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| 16384 | 64 | - | 256 | 0 | 768 | 48.29 | 22.53 | 29.35 | 44.55 | |
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## Model description |
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The model relies on Local-Sparse-Global attention to handle long sequences: |
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The model has about ~145 millions parameters (6 encoder layers - 6 decoder layers). \ |
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The model is warm started from [ccdv/lsg-bart-base-4096-pubmed](https://huggingface.co/ccdv/lsg-bart-base-4096-pubmed), converted to handle long sequences (encoder only) and fine tuned. \ |
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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: 8e-05 |
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- train_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 32 |
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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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- num_epochs: 1.0 |
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### Generate hyperparameters |
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The following hyperparameters were used during generation: |
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- dataset_name: scientific_papers |
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- dataset_config_name: pubmed |
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- eval_batch_size: 2 |
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- early_stopping: True |
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- ignore_pad_token_for_loss: True |
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- length_penalty: 2.0 |
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- max_length: 512 |
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- min_length: 128 |
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- num_beams: 5 |
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- num_samples: None |
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- no_repeat_ngram_size: None |
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- seed: 123 |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 2.1.0 |
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- Tokenizers 0.11.6 |
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