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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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metrics: |
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- rouge |
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model-index: |
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- name: flan-t5-large-extraction-cnndm_20000-all |
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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-large-extraction-cnndm_20000-all |
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This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6652 |
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- Rouge1: 35.487 |
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- Rouge2: 15.6713 |
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- Rougel: 29.9519 |
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- Rougelsum: 29.9368 |
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- Gen Len: 19.0 |
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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: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 24 |
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- seed: 1799 |
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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 | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 2.1295 | 0.08 | 200 | 1.8266 | 34.0465 | 14.7511 | 29.3395 | 29.3437 | 19.0 | |
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| 1.9354 | 0.16 | 400 | 1.7732 | 34.7923 | 15.3094 | 29.8484 | 29.8757 | 18.99 | |
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| 1.854 | 0.24 | 600 | 1.7367 | 34.8358 | 15.1969 | 29.9971 | 30.0064 | 18.986 | |
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| 1.833 | 0.32 | 800 | 1.7120 | 34.7854 | 15.5144 | 29.8141 | 29.7863 | 18.982 | |
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| 1.8217 | 0.4 | 1000 | 1.7256 | 34.7274 | 15.2763 | 30.0298 | 30.0871 | 19.0 | |
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| 1.8309 | 0.48 | 1200 | 1.7089 | 35.4328 | 15.7724 | 30.0655 | 30.0199 | 19.0 | |
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| 1.825 | 0.56 | 1400 | 1.6947 | 35.4116 | 15.6911 | 30.1438 | 30.1764 | 19.0 | |
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| 1.7914 | 0.64 | 1600 | 1.7119 | 35.5918 | 16.3762 | 30.3234 | 30.2807 | 19.0 | |
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| 1.7889 | 0.72 | 1800 | 1.6810 | 35.6413 | 15.8936 | 30.2848 | 30.2291 | 19.0 | |
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| 1.7576 | 0.8 | 2000 | 1.6826 | 35.9424 | 15.6803 | 30.5998 | 30.5571 | 19.0 | |
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| 1.7763 | 0.88 | 2200 | 1.6748 | 35.7543 | 15.984 | 30.7197 | 30.721 | 18.998 | |
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| 1.7604 | 0.96 | 2400 | 1.6652 | 35.487 | 15.6713 | 29.9519 | 29.9368 | 19.0 | |
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| 1.7138 | 1.04 | 2600 | 1.6860 | 36.0333 | 16.4065 | 30.7249 | 30.7168 | 19.0 | |
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| 1.6951 | 1.12 | 2800 | 1.6792 | 35.3149 | 15.7178 | 30.1555 | 30.1517 | 18.998 | |
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| 1.6752 | 1.2 | 3000 | 1.6832 | 34.7566 | 15.4179 | 29.7687 | 29.8259 | 19.0 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
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