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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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- multi_news |
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model-index: |
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- name: summarise_v4 |
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results: [] |
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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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# summarise_v4 |
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This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the multi_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5264 |
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- Rouge2 Precision: 0.1349 |
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- Rouge2 Recall: 0.1187 |
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- Rouge2 Fmeasure: 0.1227 |
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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: 5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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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: 1 |
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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 | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
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| 2.9616 | 0.08 | 10 | 2.8008 | 0.0552 | 0.1944 | 0.0844 | |
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| 2.7112 | 0.16 | 20 | 2.7017 | 0.1099 | 0.1212 | 0.1078 | |
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| 2.6842 | 0.24 | 30 | 2.6653 | 0.119 | 0.1252 | 0.1157 | |
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| 2.4638 | 0.32 | 40 | 2.6306 | 0.1386 | 0.1153 | 0.1222 | |
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| 2.646 | 0.4 | 50 | 2.6099 | 0.1449 | 0.1095 | 0.122 | |
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| 2.5128 | 0.48 | 60 | 2.5945 | 0.1259 | 0.1484 | 0.1313 | |
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| 2.6737 | 0.56 | 70 | 2.5832 | 0.1192 | 0.1252 | 0.118 | |
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| 2.614 | 0.64 | 80 | 2.5616 | 0.1288 | 0.1179 | 0.1193 | |
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| 2.4643 | 0.72 | 90 | 2.5612 | 0.1371 | 0.1227 | 0.124 | |
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| 2.3164 | 0.8 | 100 | 2.5606 | 0.1372 | 0.1177 | 0.1223 | |
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| 2.4514 | 0.88 | 110 | 2.5339 | 0.1412 | 0.1276 | 0.128 | |
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| 2.8113 | 0.96 | 120 | 2.5264 | 0.1349 | 0.1187 | 0.1227 | |
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### Framework versions |
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- Transformers 4.21.3 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.2.dev0 |
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- Tokenizers 0.12.1 |
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