readme: ---
  language:
  - en
  tags:
  - summarization
  datasets:
  - scientific_papers
  metrics:
  - rouge
  model-index:
  - name: ccdv/lsg-bart-base-16384-pubmed
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  **This model relies on a custom modeling file, you need to add trust_remote_code=True**\
  **See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-16384-pubmed", trust_remote_code=True)
  model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-16384-pubmed", trust_remote_code=True)
  
  text = "Replace by what you want."
  pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, device=0)
  generated_text = pipe(
    text, 
    truncation=True, 
    max_length=64, 
    no_repeat_ngram_size=7,
    num_beams=2,
    early_stopping=True
    )
  ```
  
  # ccdv/lsg-bart-base-16384-pubmed
  
  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](https://huggingface.co/datasets/scientific_papers) dataset. \
  The model is converted to handle 16384 long sequences and fine-tuned accordingly during 1 epoch. \
  It achieves the following results on the test set:
  
  | Length | Global tokens | Fine-tuning | Block Size | Sparsity | Connexions | R1    | R2    | RL    | RLsum |
  |:------ |:------------- |:----------- |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
  | 16384  | 64            | Full        | 256        | 0        | 768        | 48.32 | 22.52 | 29.36 | 44.57 |
  | 16384  | 1             | Full        | 256        | 0        | 768        | 48.26 | 22.53 | 29.40 | 44.51 |
  | 16384  | 64            | Global only | 256        | 0        | 768        | 48.12 | 20.46 | 29.34 | 44.40 |
  | 16384  | 1             | None        | 256        | 0        | 768        | 48.03 | 22.42 | 29.28 | 44.32 |
  
  Reference model:
  
  | Length | Global tokens | Fine-tuning | Block Size | Sparsity | Connexions | R1    | R2    | RL    | RLsum |
  |:------ |:------------- |:----------- |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
  | 4096   | 1             | -           | 256        | 0        | 768        | 47.37 | 21.74 | 28.59 | 43.67 |
  
  ## Model description
  The model relies on Local-Sparse-Global attention to handle long sequences:
  ![attn](attn.png)
  
  The model has about ~145 millions parameters (6 encoder layers - 6 decoder layers). \
  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.
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 8e-05
  - train_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 1.0
  
  ### Generate hyperparameters
  
  The following hyperparameters were used during generation:
  - dataset_name: scientific_papers
  - dataset_config_name: pubmed
  - eval_batch_size: 4
  - eval_samples: 6658
  - early_stopping: True
  - ignore_pad_token_for_loss: True
  - length_penalty: 2.0
  - max_length: 512
  - min_length: 128
  - num_beams: 5
  - no_repeat_ngram_size: None
  - seed: 123
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.1+cu102
  - Datasets 2.1.0
  - Tokenizers 0.11.6
  