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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-v2 |
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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-v2 |
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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: 1.0254 |
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- Rouge1: 0.244 |
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- Rouge2: 0.111 |
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- Rougel: 0.2032 |
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- Rougelsum: 0.2292 |
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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: 4 |
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- eval_batch_size: 4 |
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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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| 3.0551 | 1.0 | 500 | 2.2941 | 0.2336 | 0.1092 | 0.1969 | 0.217 | |
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| 1.6422 | 2.0 | 1000 | 1.1665 | 0.2459 | 0.1088 | 0.1991 | 0.227 | |
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| 1.4067 | 3.0 | 1500 | 1.0762 | 0.2462 | 0.1089 | 0.1982 | 0.2296 | |
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| 1.2856 | 4.0 | 2000 | 1.0518 | 0.2448 | 0.1112 | 0.2036 | 0.2298 | |
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| 1.3478 | 5.0 | 2500 | 1.0393 | 0.2458 | 0.1125 | 0.2056 | 0.2303 | |
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| 1.2114 | 6.0 | 3000 | 1.0340 | 0.2497 | 0.1145 | 0.2084 | 0.2333 | |
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| 1.3311 | 7.0 | 3500 | 1.0298 | 0.2479 | 0.1143 | 0.207 | 0.233 | |
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| 1.3081 | 8.0 | 4000 | 1.0270 | 0.2448 | 0.1112 | 0.2035 | 0.2301 | |
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| 1.1794 | 9.0 | 4500 | 1.0258 | 0.2449 | 0.1112 | 0.2036 | 0.2301 | |
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| 1.2407 | 10.0 | 5000 | 1.0254 | 0.244 | 0.111 | 0.2032 | 0.2292 | |
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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 |
