readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - ccdv/arxiv-summarization
  metrics:
  - rouge
  model-index:
  - name: arxiv-summarization-t5-small-2022-09-05
    results:
    - task:
        name: Summarization
        type: summarization
      dataset:
        name: ccdv/arxiv-summarization
        type: ccdv/arxiv-summarization
        config: section
        split: train
        args: section
      metrics:
      - name: Rouge1
        type: rouge
        value: 37.7982
  ---
  
  <!-- 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. -->
  
  # arxiv-summarization-t5-small-2022-09-05
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the ccdv/arxiv-summarization dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.2994
  - Rouge1: 37.7982
  - Rouge2: 12.5571
  - Rougel: 24.9325
  - Rougelsum: 32.9189
  - Gen Len: 122.0061
  
  ## Model description
  
  More information needed
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
  | 2.5925        | 0.39  | 10000 | 2.4566          | 17.8432 | 6.6779 | 14.2303 | 16.1952   | 19.0    |
  | 2.518         | 0.79  | 20000 | 2.3868          | 18.0354 | 6.8565 | 14.3552 | 16.3664   | 19.0    |
  | 2.4587        | 1.18  | 30000 | 2.3600          | 18.2076 | 6.9618 | 14.5349 | 16.5626   | 19.0    |
  | 2.4365        | 1.58  | 40000 | 2.3295          | 18.3579 | 7.0312 | 14.6145 | 16.6845   | 19.0    |
  | 2.4306        | 1.97  | 50000 | 2.3190          | 18.4551 | 7.0861 | 14.6879 | 16.7627   | 19.0    |
  | 2.4005        | 2.37  | 60000 | 2.3056          | 18.3521 | 7.0496 | 14.6413 | 16.6832   | 19.0    |
  | 2.396         | 2.76  | 70000 | 2.3012          | 18.348  | 7.0439 | 14.6509 | 16.6994   | 19.0    |
  
  
  ### Framework versions
  
  - Transformers 4.22.0.dev0
  - Pytorch 1.12.1+cu102
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  