readme: ---
  language:
  - en
  tags:
  - summarization
  datasets:
  - scientific_papers
  metrics:
  - rouge
  model-index:
  - name: ccdv/lsg-bart-base-4096-arxiv
    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-4096-arxiv", trust_remote_code=True)
  model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-arxiv", 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-4096-arxiv
  
  This model is a fine-tuned version of [ccdv/lsg-bart-base-4096](https://huggingface.co/ccdv/lsg-bart-base-4096) on the [scientific_papers arxiv](https://huggingface.co/datasets/scientific_papers) dataset. \
  It achieves the following results on the test set:
  
  | Length | Sparse Type  | Block Size | Sparsity | Connexions | R1    | R2    | RL    | RLsum |
  |:------ |:------------ |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
  | 4096   | Local        | 256        | 0        | 768        | 46.65 | 18.91 | 26.90 | 42.18 |
  | 4096   | Local        | 128        | 0        | 384        | 46.18 | 18.57 | 26.71 | 41.69 |
  | 4096   | Pooling      | 128        | 4        | 644        | 46.27 | 18.68 | 26.87 | 41.82 |
  | 4096   | Stride       | 128        | 4        | 644        | 46.34 | 18.64 | 26.69 | 41.87 |
  | 4096   | Block Stride | 128        | 4        | 644        | 46.23 | 18.62 | 26.62 | 41.80 |
  | 4096   | Norm         | 128        | 4        | 644        | 45.96 | 18.46 | 26.52 | 41.51 |
  | 4096   | LSH          | 128        | 4        | 644        | 46.19 | 18.72 | 26.89 | 41.76 |
  
  With smaller block size (lower ressources):
  
  | Length | Sparse Type  | Block Size | Sparsity | Connexions | R1    | R2    | RL    | RLsum |
  |:------ |:------------ |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
  | 4096   | Local        | 64         | 0        | 192        | 44.71 | 17.53 | 26.03 | 40.23 |
  | 4096   | Local        | 32         | 0        | 96         | 39.67 | 14.34 | 23.81 | 35.00 |
  | 4096   | Pooling      | 32         | 4        | 160        | 42.75 | 16.34 | 25.20 | 38.23 |
  | 4096   | Stride       | 32         | 4        | 160        | 44.23 | 17.21 | 25.71 | 39.72 |
  | 4096   | Block Stride | 32         | 4        | 160        | 44.15 | 17.10 | 25.68 | 39.60 |
  | 4096   | Norm         | 32         | 4        | 160        | 42.02 | 15.65 | 24.56 | 37.45 |
  | 4096   | LSH          | 32         | 4        | 160        | 42.58 | 16.21 | 25.10 | 38.04 |
  
  ## 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 BART-base, 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: 6.0
  
  ### Generate hyperparameters
  
  The following hyperparameters were used during generation:
  - dataset_name: scientific_papers
  - dataset_config_name: arxiv
  - eval_batch_size: 8
  - eval_samples: 6440
  - early_stopping: True
  - ignore_pad_token_for_loss: True
  - length_penalty: 2.0
  - max_length: 320
  - min_length: 32
  - 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
  