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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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datasets: |
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- govreport-summarization |
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metrics: |
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- rouge |
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model-index: |
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- name: led-large-16384-govreport |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: govreport-summarization |
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type: govreport-summarization |
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config: document |
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split: validation |
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args: document |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.5444603858958118 |
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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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# led-large-16384-govreport |
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This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the govreport-summarization dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1142 |
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- Rouge1: 0.5445 |
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- Rouge2: 0.2225 |
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- Rougel: 0.2578 |
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- Rougelsum: 0.2579 |
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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: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 64 |
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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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- num_epochs: 100 |
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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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| 1.8152 | 3.65 | 500 | 1.7956 | 0.5095 | 0.2040 | 0.2382 | 0.2381 | |
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| 1.6981 | 3.66 | 1000 | 1.7624 | 0.5194 | 0.2107 | 0.2437 | 0.2437 | |
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| 1.7048 | 5.49 | 1500 | 1.7448 | 0.5253 | 0.2149 | 0.2467 | 0.2467 | |
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| 1.6469 | 7.32 | 2000 | 1.7416 | 0.5299 | 0.2177 | 0.2499 | 0.2500 | |
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| 1.6465 | 9.15 | 2500 | 1.7318 | 0.5299 | 0.2160 | 0.2476 | 0.2478 | |
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| 1.578 | 10.98 | 3000 | 1.7254 | 0.5321 | 0.2192 | 0.2529 | 0.2530 | |
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| 1.5631 | 12.81 | 3500 | 1.7189 | 0.5309 | 0.2170 | 0.2520 | 0.2520 | |
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| 1.5641 | 14.63 | 4000 | 1.7152 | 0.5343 | 0.2198 | 0.2550 | 0.2550 | |
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| 1.4753 | 16.48 | 4500 | 1.7181 | 0.5305 | 0.2179 | 0.2539 | 0.2542 | |
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| 1.4792 | 18.3 | 5000 | 1.7152 | 0.5375 | 0.2258 | 0.2586 | 0.2588 | |
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| 1.4206 | 20.13 | 5500 | 1.7142 | 0.5366 | 0.2216 | 0.2555 | 0.2556 | |
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| 1.4273 | 21.96 | 6000 | 1.7128 | 0.5364 | 0.2232 | 0.2573 | 0.2573 | |
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| 1.4078 | 23.78 | 6500 | 1.7114 | 0.5344 | 0.2200 | 0.2562 | 0.2563 | |
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| 1.355 | 25.61 | 7000 | 1.7153 | 0.5354 | 0.2212 | 0.2564 | 0.2564 | |
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| 1.409 | 27.44 | 7500 | 1.7119 | 0.5363 | 0.2217 | 0.2568 | 0.2570 | |
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| 1.3817 | 29.26 | 8000 | 1.7166 | 0.5369 | 0.2229 | 0.2582 | 0.2582 | |
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| 1.3072 | 31.13 | 8500 | 1.7302 | 0.5379 | 0.2249 | 0.2604 | 0.2603 | |
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| 1.3172 | 32.96 | 9000 | 1.7121 | 0.5377 | 0.2236 | 0.2588 | 0.2587 | |
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| 1.277 | 34.78 | 9500 | 1.7255 | 0.5368 | 0.2221 | 0.2584 | 0.2583 | |
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| 1.1849 | 36.61 | 10000 | 1.7438 | 0.5382 | 0.2244 | 0.2611 | 0.2612 | |
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| 1.1565 | 38.44 | 10500 | 1.7540 | 0.5414 | 0.2258 | 0.2612 | 0.2612 | |
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| 1.1415 | 40.26 | 11000 | 1.7707 | 0.5401 | 0.2251 | 0.2618 | 0.2618 | |
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| 1.085 | 42.09 | 11500 | 1.7791 | 0.5401 | 0.2235 | 0.2595 | 0.2595 | |
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| 1.088 | 43.92 | 12000 | 1.7869 | 0.5422 | 0.2265 | 0.2616 | 0.2615 | |
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| 1.0678 | 45.74 | 12500 | 1.8058 | 0.5420 | 0.2253 | 0.2607 | 0.2607 | |
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| 1.0815 | 47.57 | 13000 | 1.8186 | 0.5405 | 0.2248 | 0.2615 | 0.2615 | |
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| 1.0456 | 49.4 | 13500 | 1.8346 | 0.5430 | 0.2262 | 0.2619 | 0.2618 | |
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| 0.9553 | 51.22 | 14000 | 1.8449 | 0.5387 | 0.2239 | 0.2614 | 0.2613 | |
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| 0.958 | 53.05 | 14500 | 1.8716 | 0.5438 | 0.2274 | 0.2618 | 0.2618 | |
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| 0.9213 | 54.88 | 15000 | 1.8780 | 0.5438 | 0.2249 | 0.2612 | 0.2612 | |
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| 0.876 | 56.77 | 15500 | 1.8904 | 0.5439 | 0.2253 | 0.2621 | 0.2621 | |
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| 0.8967 | 58.6 | 16000 | 1.9085 | 0.5439 | 0.2264 | 0.2634 | 0.2633 | |
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| 0.9138 | 60.43 | 16500 | 1.9089 | 0.5428 | 0.2242 | 0.2597 | 0.2597 | |
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| 0.848 | 62.25 | 17000 | 1.9153 | 0.5441 | 0.2242 | 0.2600 | 0.2599 | |
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| 0.7804 | 64.08 | 17500 | 1.9311 | 0.5422 | 0.2241 | 0.2603 | 0.2604 | |
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| 0.8326 | 65.91 | 18000 | 1.9391 | 0.5446 | 0.2242 | 0.2604 | 0.2602 | |
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| 0.8164 | 67.73 | 18500 | 1.9607 | 0.5430 | 0.2245 | 0.2607 | 0.2607 | |
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| 0.8129 | 69.56 | 19000 | 1.9731 | 0.5456 | 0.2277 | 0.2633 | 0.2633 | |
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| 0.8049 | 71.39 | 19500 | 1.9804 | 0.5433 | 0.2248 | 0.2618 | 0.2619 | |
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| 0.7605 | 73.21 | 20000 | 2.0060 | 0.5449 | 0.2256 | 0.2607 | 0.2606 | |
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| 0.7595 | 75.04 | 20500 | 2.0085 | 0.5425 | 0.2227 | 0.2590 | 0.2590 | |
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| 0.7837 | 76.87 | 21000 | 2.0073 | 0.5441 | 0.2243 | 0.2608 | 0.2609 | |
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| 0.7458 | 78.69 | 21500 | 2.0210 | 0.5447 | 0.2260 | 0.2619 | 0.2621 | |
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| 0.7235 | 80.52 | 22000 | 2.0273 | 0.5445 | 0.2253 | 0.2610 | 0.2611 | |
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| 0.7405 | 82.35 | 22500 | 2.0405 | 0.5438 | 0.2243 | 0.2600 | 0.2599 | |
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| 0.7323 | 84.17 | 23000 | 2.0385 | 0.5466 | 0.2256 | 0.2607 | 0.2608 | |
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| 0.7333 | 86.0 | 23500 | 2.0386 | 0.5447 | 0.2248 | 0.2608 | 0.2609 | |
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| 0.7067 | 87.83 | 24000 | 2.0582 | 0.5449 | 0.2243 | 0.2601 | 0.2600 | |
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| 0.7073 | 89.65 | 24500 | 2.0615 | 0.5455 | 0.2253 | 0.2604 | 0.2603 | |
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| 0.6903 | 91.48 | 25000 | 2.0657 | 0.5482 | 0.2273 | 0.2627 | 0.2626 | |
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| 0.7203 | 93.31 | 25500 | 2.0574 | 0.5452 | 0.2241 | 0.2596 | 0.2597 | |
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| 0.6765 | 95.13 | 26000 | 2.0692 | 0.5437 | 0.2249 | 0.2608 | 0.2608 | |
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| 0.6959 | 96.96 | 26500 | 2.0696 | 0.5442 | 0.2246 | 0.2614 | 0.2614 | |
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| 0.6918 | 98.79 | 27000 | 2.0701 | 0.5444 | 0.2252 | 0.2615 | 0.2615 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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