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update model card README.md
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README.md
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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_v9
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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_v9
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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: 2.3650
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- Rouge1 Precision: 0.4673
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- Rouge1 Recall: 0.4135
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- Rouge1 Fmeasure: 0.4263
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- Rouge2 Precision: 0.1579
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- Rouge2 Recall: 0.1426
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- Rouge2 Fmeasure: 0.1458
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- Rougel Precision: 0.2245
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- Rougel Recall: 0.2008
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- Rougel Fmeasure: 0.2061
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- Rougelsum Precision: 0.2245
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- Rougelsum Recall: 0.2008
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- Rougelsum Fmeasure: 0.2061
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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: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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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: 2
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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 | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure |
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|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|
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| 2.8095 | 0.16 | 10 | 2.5393 | 0.287 | 0.5358 | 0.3674 | 0.1023 | 0.1917 | 0.1311 | 0.1374 | 0.2615 | 0.1771 | 0.1374 | 0.2615 | 0.1771 |
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| 2.6056 | 0.32 | 20 | 2.4752 | 0.5005 | 0.3264 | 0.3811 | 0.1663 | 0.1054 | 0.1249 | 0.2582 | 0.1667 | 0.1957 | 0.2582 | 0.1667 | 0.1957 |
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| 2.5943 | 0.48 | 30 | 2.4422 | 0.4615 | 0.3833 | 0.4047 | 0.1473 | 0.1273 | 0.1321 | 0.2242 | 0.1885 | 0.1981 | 0.2242 | 0.1885 | 0.1981 |
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| 2.4842 | 0.64 | 40 | 2.4186 | 0.4675 | 0.3829 | 0.4081 | 0.1581 | 0.1294 | 0.1384 | 0.2286 | 0.187 | 0.1995 | 0.2286 | 0.187 | 0.1995 |
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| 2.4454 | 0.8 | 50 | 2.3990 | 0.467 | 0.408 | 0.4222 | 0.1633 | 0.1429 | 0.1477 | 0.2294 | 0.2008 | 0.2076 | 0.2294 | 0.2008 | 0.2076 |
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| 2.3622 | 0.96 | 60 | 2.3857 | 0.4567 | 0.3898 | 0.41 | 0.1433 | 0.1233 | 0.1295 | 0.2205 | 0.1876 | 0.1976 | 0.2205 | 0.1876 | 0.1976 |
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| 2.4034 | 1.13 | 70 | 2.3835 | 0.4515 | 0.4304 | 0.4294 | 0.1526 | 0.1479 | 0.1459 | 0.2183 | 0.209 | 0.2078 | 0.2183 | 0.209 | 0.2078 |
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| 2.2612 | 1.29 | 80 | 2.3804 | 0.455 | 0.4193 | 0.4236 | 0.1518 | 0.1429 | 0.1427 | 0.2177 | 0.2025 | 0.2037 | 0.2177 | 0.2025 | 0.2037 |
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| 2.2563 | 1.45 | 90 | 2.3768 | 0.4821 | 0.391 | 0.4196 | 0.1652 | 0.1357 | 0.144 | 0.2385 | 0.1929 | 0.2069 | 0.2385 | 0.1929 | 0.2069 |
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| 2.243 | 1.61 | 100 | 2.3768 | 0.4546 | 0.4093 | 0.4161 | 0.1552 | 0.1402 | 0.1422 | 0.2248 | 0.2016 | 0.2052 | 0.2248 | 0.2016 | 0.2052 |
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| 2.2505 | 1.77 | 110 | 2.3670 | 0.4625 | 0.4189 | 0.4262 | 0.1606 | 0.1485 | 0.1493 | 0.2301 | 0.2098 | 0.2119 | 0.2301 | 0.2098 | 0.2119 |
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| 2.2453 | 1.93 | 120 | 2.3650 | 0.4673 | 0.4135 | 0.4263 | 0.1579 | 0.1426 | 0.1458 | 0.2245 | 0.2008 | 0.2061 | 0.2245 | 0.2008 | 0.2061 |
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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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