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xsum_and_billsum_and_samsum_old
psin/xsum_and_billsum_and_samsum_old
xsum_and_billsum_and_samsum_old is a machine learning model from psin. 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.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- t5
- Access
- Public
- Created
- May 10, 2023
- Updated
- May 10, 2023
- SHA
- bcf2fba2
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. -->
xsum_and_billsum_and_samsum_old
This model is a fine-tuned version of psin/xsum_and_billsum_and_samsum on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.6572
- Rouge1: 0.0873
- Rouge2: 0.0186
- Rougel: 0.0712
- Rougelsum: 0.0711
- Gen Len: 19.0
- Precision: 0.069
- Recall: -0.2018
- F1: -0.0
- Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled
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: 8
- eval_batch_size: 8
- seed: 72
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1 | Hashcode |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 42 | 3.7360 | 0.0789 | 0.0152 | 0.0679 | 0.068 | 19.0 | 0.071 | -0.2095 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log | 2.0 | 84 | 3.6936 | 0.085 | 0.0193 | 0.071 | 0.0712 | 19.0 | 0.0898 | -0.2007 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log | 3.0 | 126 | 3.6739 | 0.0854 | 0.0202 | 0.0712 | 0.0713 | 19.0 | 0.0973 | -0.1935 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log | 4.0 | 168 | 3.6643 | 0.0865 | 0.0194 | 0.0709 | 0.0709 | 19.0 | 0.0845 | -0.197 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log | 5.0 | 210 | 3.6589 | 0.087 | 0.0186 | 0.0711 | 0.0711 | 19.0 | 0.0698 | -0.2012 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
| No log | 6.0 | 252 | 3.6572 | 0.0873 | 0.0186 | 0.0712 | 0.0711 | 19.0 | 0.069 | -0.2018 | -0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Library
- transformers
- Type
- t5
- Created
- May 10, 2023
- Updated
- May 10, 2023