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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- Summarization |
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- generated_from_trainer |
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datasets: |
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- cnn_dailymail |
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metrics: |
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- rouge |
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base_model: google/flan-t5-base |
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model-index: |
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- name: flan-t5-base-finetuned-QLoRA-10000 |
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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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# flan-t5-base-finetuned-QLoRA-10000 |
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the cnn_dailymail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0625 |
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- Rouge1: 0.2397 |
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- Rouge2: 0.1107 |
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- Rougel: 0.1948 |
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- Rougelsum: 0.226 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 1.3695 | 1.0 | 1250 | 1.1374 | 0.232 | 0.1046 | 0.1884 | 0.218 | |
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| 1.197 | 2.0 | 2500 | 1.0885 | 0.2371 | 0.1093 | 0.1934 | 0.2236 | |
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| 1.1489 | 3.0 | 3750 | 1.0765 | 0.2389 | 0.1098 | 0.1939 | 0.2248 | |
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| 1.156 | 4.0 | 5000 | 1.0693 | 0.2403 | 0.1107 | 0.195 | 0.226 | |
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| 1.1135 | 5.0 | 6250 | 1.0663 | 0.2393 | 0.1102 | 0.1944 | 0.2252 | |
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| 1.1607 | 6.0 | 7500 | 1.0648 | 0.24 | 0.1109 | 0.1951 | 0.2259 | |
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| 1.1222 | 7.0 | 8750 | 1.0635 | 0.2398 | 0.1106 | 0.1947 | 0.2256 | |
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| 1.1619 | 8.0 | 10000 | 1.0629 | 0.2399 | 0.1106 | 0.1949 | 0.2259 | |
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| 1.1366 | 9.0 | 11250 | 1.0626 | 0.2397 | 0.1108 | 0.1948 | 0.226 | |
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| 1.2062 | 10.0 | 12500 | 1.0625 | 0.2397 | 0.1107 | 0.1948 | 0.226 | |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.1 |
