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update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- kp20k
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metrics:
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- rouge
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model-index:
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- name: keyphrase-extractions_bart-large
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results:
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- task:
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name: Sequence-to-sequence Language Modeling
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type: text2text-generation
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dataset:
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name: kp20k
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type: kp20k
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config: generation
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split: train[:15%]
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args: generation
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metrics:
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- name: Rouge1
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type: rouge
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value: 0.4713
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# keyphrase-extractions_bart-large
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This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the kp20k dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.7257
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- Rouge1: 0.4713
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- Rouge2: 0.2385
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- Rougel: 0.384
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- Rougelsum: 0.3841
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- Gen Len: 18.3164
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- Phrase match: 0.1917
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Phrase match |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------------:|
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| 2.5104 | 1.0 | 730 | 1.8021 | 0.464 | 0.2336 | 0.3765 | 0.3766 | 18.9074 | 0.1784 |
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| 1.8436 | 2.0 | 1460 | 1.7473 | 0.4709 | 0.2381 | 0.3834 | 0.3836 | 17.8127 | 0.1891 |
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| 1.6864 | 3.0 | 2190 | 1.7257 | 0.4713 | 0.2385 | 0.384 | 0.3841 | 18.3164 | 0.1917 |
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### Framework versions
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- Transformers 4.26.0
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- Pytorch 1.13.1+cu116
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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