---
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