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bart-large-xsum-finetuned-en-sum-2
october-sd/bart-large-xsum-finetuned-en-sum-2
bart-large-xsum-finetuned-en-sum-2 is a summarization model from october-sd. Use it when you need a shorter version of a longer text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Summarization
- Library
- transformers
- License
- mit
- Model type
- bart
- Access
- Public
- Created
- Mar 17, 2024
- Updated
- Mar 17, 2024
- SHA
- e6597e90
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Base models
Model card
From the Hugging Face model README
<!-- 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-large-xsum-finetuned-en-sum-2
This model is a fine-tuned version of october-sd/bart-large-xsum-finetuned-en-sum on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6259
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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.99 | 126 | 1.6222 |
| No log | 1.98 | 252 | 1.6259 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.2
- Task
- Summarization
- Library
- transformers
- Type
- bart
- License
- mit
- Created
- Mar 17, 2024
- Updated
- Mar 17, 2024