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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 |
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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 |
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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: 1.0780 |
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- Rouge1: 0.2435 |
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- Rouge2: 0.1079 |
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- Rougel: 0.1991 |
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- Rougelsum: 0.2302 |
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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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| 12.7942 | 1.0 | 250 | 10.7766 | 0.2346 | 0.1022 | 0.1834 | 0.2154 | |
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| 3.0774 | 2.0 | 500 | 2.5061 | 0.2351 | 0.1094 | 0.197 | 0.2204 | |
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| 2.1947 | 3.0 | 750 | 1.4702 | 0.2403 | 0.1104 | 0.1997 | 0.2261 | |
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| 1.7687 | 4.0 | 1000 | 1.2326 | 0.247 | 0.1148 | 0.2024 | 0.2307 | |
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| 1.4731 | 5.0 | 1250 | 1.1516 | 0.2538 | 0.1203 | 0.2074 | 0.2381 | |
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| 1.4802 | 6.0 | 1500 | 1.1120 | 0.2432 | 0.1102 | 0.1993 | 0.2271 | |
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| 1.3568 | 7.0 | 1750 | 1.0945 | 0.2427 | 0.1089 | 0.1991 | 0.2279 | |
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| 1.4054 | 8.0 | 2000 | 1.0843 | 0.2428 | 0.1076 | 0.1993 | 0.2293 | |
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| 1.3151 | 9.0 | 2250 | 1.0795 | 0.2432 | 0.1076 | 0.1991 | 0.2299 | |
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| 1.2669 | 10.0 | 2500 | 1.0780 | 0.2435 | 0.1079 | 0.1991 | 0.2302 | |
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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 |
