readme: ---
  language:
  - en
  tags:
  - summarization
  datasets:
  - ccdv/mediasum
  metrics:
  - rouge
  model-index:
  - name: ccdv/lsg-bart-base-4096-mediasum
    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-mediasum", trust_remote_code=True)
  model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-mediasum", 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-mediasum
  
  This model is a fine-tuned version of [ccdv/lsg-bart-base-4096](https://huggingface.co/ccdv/lsg-bart-base-4096) on the [ccdv/mediasum roberta_prepended](https://huggingface.co/datasets/ccdv/mediasum) 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        | 35.16 | 18.13 | 31.54 | 32.20 |
  | 4096   | Local        | 128        | 0        | 384        | 34.16 | 17.61 | 30.75 | 31.41 |
  | 4096   | Pooling      | 128        | 4        | 644        | 34.52 | 17.71 | 31.01 | 31.67 |
  | 4096   | Stride       | 128        | 4        | 644        | 35.05 | 18.11 | 31.47 | 32.13 |
  | 4096   | Block Stride | 128        | 4        | 644        | 34.72 | 17.81 | 31.13 | 31.82 |
  | 4096   | Norm         | 128        | 4        | 644        | 34.75 | 17.86 | 31.10 | 31.77 |
  | 4096   | LSH          | 128        | 4        | 644        | 34.54 | 17.81 | 31.05 | 31.71 |
  
  With smaller block size (lower ressources):
  
  | Length | Sparse Type  | Block Size | Sparsity | Connexions | R1    | R2    | RL    | RLsum |
  |:------ |:------------ |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
  | 4096   | Local        | 64         | 0        | 192        | 32.55 | 16.66 | 29.36 | 30.00 |
  | 4096   | Local        | 32         | 0        | 96         | 30.98 | 15.41 | 27.84 | 28.46 |
  | 4096   | Pooling      | 32         | 4        | 160        | 31.84 | 16.02 | 28.68 | 29.30 |
  | 4096   | Stride       | 32         | 4        | 160        | 32.67 | 16.68 | 29.47 | 30.10 |
  | 4096   | Block Stride | 32         | 4        | 160        | 32.51 | 16.64 | 29.33 | 29.94 |
  | 4096   | Norm         | 32         | 4        | 160        | 32.44 | 16.48 | 29.20 | 29.79 |
  | 4096   | LSH          | 32         | 4        | 160        | 31.79 | 16.04 | 28.67 | 29.31 |
  
  ## 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: ccdv/mediasum
  - dataset_config_name: roberta_prepended
  - eval_batch_size: 8
  - eval_samples: 10000
  - early_stopping: True
  - ignore_pad_token_for_loss: True
  - length_penalty: 2.0
  - max_length: 128
  - min_length: 3
  - 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
  