bart-base-xsum / README.md
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Add evaluation results on the default config and test split of xsum (#6)
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---
license: apache-2.0
tags:
- generated_from_trainer
- summarization
base_model: facebook/bart-base
model-index:
- name: bart-base-xsum
results:
- task:
type: summarization
name: Summarization
dataset:
name: xsum
type: xsum
config: default
split: test
metrics:
- type: rouge
value: 38.6513
name: ROUGE-1
verified: true
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value: 17.7585
name: ROUGE-2
verified: true
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- type: rouge
value: 32.2033
name: ROUGE-L
verified: true
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- type: rouge
value: 32.2064
name: ROUGE-LSUM
verified: true
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value: 1.822434902191162
name: loss
verified: true
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- type: gen_len
value: 19.7028
name: gen_len
verified: true
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- task:
type: summarization
name: Summarization
dataset:
name: xsum
type: xsum
config: default
split: validation
metrics:
- type: rouge
value: 38.7415
name: ROUGE-1
verified: true
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verified: true
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- type: rouge
value: 32.2861
name: ROUGE-L
verified: true
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- type: rouge
value: 32.2763
name: ROUGE-LSUM
verified: true
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- type: loss
value: 1.8132821321487427
name: loss
verified: true
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name: gen_len
verified: true
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- task:
type: summarization
name: Summarization
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: test
metrics:
- type: rouge
value: 14.9833
name: ROUGE-1
verified: true
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name: ROUGE-2
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---
<!-- 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. -->
# bart-base-xsum
**Training:** The model has been trained using the script provided in the following repository https://github.com/MorenoLaQuatra/transformers-tasks-templates
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on [xsum](https://huggingface.co/datasets/xsum) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8051
- R1: 0.5643
- R2: 0.3017
- Rl: 0.5427
- Rlsum: 0.5427
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | R1 | R2 | Rl | Rlsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|
| 0.8983 | 1.0 | 6377 | 0.8145 | 0.5443 | 0.2724 | 0.5212 | 0.5211 |
| 0.8211 | 2.0 | 12754 | 0.7940 | 0.5519 | 0.2831 | 0.5295 | 0.5295 |
| 0.7701 | 3.0 | 19131 | 0.7839 | 0.5569 | 0.2896 | 0.5347 | 0.5348 |
| 0.7046 | 4.0 | 25508 | 0.7792 | 0.5615 | 0.2956 | 0.5394 | 0.5393 |
| 0.6837 | 5.0 | 31885 | 0.7806 | 0.5631 | 0.2993 | 0.5416 | 0.5416 |
| 0.6412 | 6.0 | 38262 | 0.7816 | 0.5643 | 0.301 | 0.5427 | 0.5426 |
| 0.6113 | 7.0 | 44639 | 0.7881 | 0.5645 | 0.3017 | 0.5428 | 0.5428 |
| 0.5855 | 8.0 | 51016 | 0.7921 | 0.5651 | 0.303 | 0.5433 | 0.5432 |
| 0.5636 | 9.0 | 57393 | 0.7972 | 0.5649 | 0.3032 | 0.5433 | 0.5433 |
| 0.5482 | 10.0 | 63770 | 0.7996 | 0.565 | 0.3036 | 0.5436 | 0.5435 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6