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bart-abs-2409-1947-lr-3e-06-bs-8-maxep-10
roequitz/bart-abs-2409-1947-lr-3e-06-bs-8-maxep-10
bart-abs-2409-1947-lr-3e-06-bs-8-maxep-10 is a machine learning model from roequitz. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- bart
- Access
- Public
- Created
- Sep 25, 2024
- Updated
- Sep 25, 2024
- SHA
- e4f48c2c
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-abs-2409-1947-lr-3e-06-bs-8-maxep-10
This model is a fine-tuned version of sshleifer/distilbart-xsum-12-6 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 7.8363
- Rouge/rouge1: 0.3097
- Rouge/rouge2: 0.0856
- Rouge/rougel: 0.2463
- Rouge/rougelsum: 0.2464
- Bertscore/bertscore-precision: 0.8589
- Bertscore/bertscore-recall: 0.8656
- Bertscore/bertscore-f1: 0.8622
- Meteor: 0.2246
- Gen Len: 36.0
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: 3e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1784 | 1.0 | 109 | 7.8164 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 |
| 0.1816 | 2.0 | 218 | 7.8349 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 44.0 |
| 0.1813 | 3.0 | 327 | 7.8471 | 0.2677 | 0.0541 | 0.2002 | 0.2007 | 0.8757 | 0.864 | 0.8697 | 0.25 | 37.0 |
| 0.1798 | 4.0 | 436 | 7.8598 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 |
| 0.2 | 5.0 | 545 | 7.8568 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 44.0 |
| 0.2047 | 6.0 | 654 | 7.8458 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 |
| 0.2066 | 7.0 | 763 | 7.8409 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 |
| 0.2144 | 8.0 | 872 | 7.8382 | 0.2646 | 0.0434 | 0.203 | 0.2031 | 0.867 | 0.8616 | 0.8642 | 0.2187 | 36.0 |
| 0.2062 | 9.0 | 981 | 7.8368 | 0.2646 | 0.0434 | 0.203 | 0.2031 | 0.867 | 0.8616 | 0.8642 | 0.2187 | 36.0 |
| 0.2056 | 10.0 | 1090 | 7.8363 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Created
- Sep 25, 2024
- Updated
- Sep 25, 2024