readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - kp20k
  metrics:
  - rouge
  model-index:
  - name: keyphrase-extractions_bart-large
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: kp20k
        type: kp20k
        config: generation
        split: train[:15%]
        args: generation
      metrics:
      - name: Rouge1
        type: rouge
        value: 0.4713
  ---
  
  <!-- 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. -->
  
  # keyphrase-extractions_bart-large
  
  This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the kp20k dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.7257
  - Rouge1: 0.4713
  - Rouge2: 0.2385
  - Rougel: 0.384
  - Rougelsum: 0.3841
  - Gen Len: 18.3164
  - Phrase match: 0.1917
  
  ## 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: 2e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Phrase match |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------------:|
  | 2.5104        | 1.0   | 730  | 1.8021          | 0.464  | 0.2336 | 0.3765 | 0.3766    | 18.9074 | 0.1784       |
  | 1.8436        | 2.0   | 1460 | 1.7473          | 0.4709 | 0.2381 | 0.3834 | 0.3836    | 17.8127 | 0.1891       |
  | 1.6864        | 3.0   | 2190 | 1.7257          | 0.4713 | 0.2385 | 0.384  | 0.3841    | 18.3164 | 0.1917       |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  