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--- |
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license: apache-2.0 |
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base_model: facebook/bart-base |
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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: all_9843_bart-base |
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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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# all_9843_bart-base |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2356 |
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- Rouge1: 0.2722 |
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- Rouge2: 0.1239 |
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- Rougel: 0.2315 |
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- Rougelsum: 0.2424 |
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- Gen Len: 19.9567 |
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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: 16 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 500 |
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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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| 2.759 | 0.89 | 500 | 1.2348 | 0.2615 | 0.1071 | 0.2187 | 0.2283 | 19.956 | |
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| 1.0891 | 1.78 | 1000 | 1.2122 | 0.2667 | 0.1145 | 0.224 | 0.2351 | 19.9713 | |
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| 0.9877 | 2.67 | 1500 | 1.2076 | 0.2701 | 0.118 | 0.2271 | 0.238 | 19.9413 | |
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| 0.9299 | 3.56 | 2000 | 1.2072 | 0.2682 | 0.1205 | 0.2267 | 0.2385 | 19.9667 | |
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| 0.8841 | 4.44 | 2500 | 1.2088 | 0.2711 | 0.1213 | 0.2294 | 0.2406 | 19.956 | |
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| 0.8425 | 5.33 | 3000 | 1.2154 | 0.2718 | 0.1245 | 0.2317 | 0.2426 | 19.9673 | |
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| 0.8123 | 6.22 | 3500 | 1.2276 | 0.2719 | 0.1242 | 0.2315 | 0.2422 | 19.958 | |
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| 0.7876 | 7.11 | 4000 | 1.2259 | 0.2726 | 0.1228 | 0.2311 | 0.242 | 19.9647 | |
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| 0.769 | 8.0 | 4500 | 1.2244 | 0.2733 | 0.126 | 0.2324 | 0.2436 | 19.9667 | |
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| 0.75 | 8.89 | 5000 | 1.2313 | 0.2723 | 0.1236 | 0.231 | 0.2422 | 19.964 | |
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| 0.7369 | 9.78 | 5500 | 1.2356 | 0.2722 | 0.1239 | 0.2315 | 0.2424 | 19.9567 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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