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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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- billsum |
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metrics: |
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- rouge |
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- bleu |
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model-index: |
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- name: t5-small-billsum_model |
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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: billsum |
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type: billsum |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.2444 |
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- name: Bleu |
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type: bleu |
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value: 0.0018 |
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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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# t5-small-billsum_model |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4109 |
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- Rouge1: 0.2444 |
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- Rouge2: 0.2013 |
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- Rougel: 0.2371 |
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- Rougelsum: 0.2372 |
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- Gen Len: 18.9994 |
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- Bleu: 0.0018 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: 40 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:| |
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| No log | 1.0 | 296 | 1.8388 | 0.222 | 0.1724 | 0.2118 | 0.2119 | 18.9942 | 0.0011 | |
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| 2.2619 | 2.0 | 592 | 1.7177 | 0.2287 | 0.1797 | 0.2186 | 0.2187 | 18.9957 | 0.0013 | |
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| 2.2619 | 3.0 | 888 | 1.6596 | 0.2337 | 0.1853 | 0.2241 | 0.2242 | 18.9979 | 0.0015 | |
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| 1.9013 | 4.0 | 1185 | 1.6184 | 0.2359 | 0.1873 | 0.2268 | 0.2269 | 19.0 | 0.0016 | |
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| 1.9013 | 5.0 | 1481 | 1.5934 | 0.2372 | 0.1891 | 0.2285 | 0.2286 | 19.0 | 0.0016 | |
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| 1.8196 | 6.0 | 1777 | 1.5683 | 0.2378 | 0.1901 | 0.2293 | 0.2294 | 18.9985 | 0.0016 | |
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| 1.7679 | 7.0 | 2073 | 1.5489 | 0.2371 | 0.1901 | 0.2288 | 0.2289 | 19.0 | 0.0016 | |
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| 1.7679 | 8.0 | 2370 | 1.5335 | 0.2386 | 0.1924 | 0.2306 | 0.2306 | 19.0 | 0.0017 | |
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| 1.7315 | 9.0 | 2666 | 1.5215 | 0.239 | 0.193 | 0.2311 | 0.2312 | 19.0 | 0.0017 | |
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| 1.7315 | 10.0 | 2962 | 1.5094 | 0.2394 | 0.1938 | 0.2318 | 0.2317 | 19.0 | 0.0017 | |
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| 1.6994 | 11.0 | 3258 | 1.4996 | 0.2403 | 0.195 | 0.2325 | 0.2325 | 19.0 | 0.0017 | |
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| 1.6769 | 12.0 | 3555 | 1.4904 | 0.2405 | 0.1955 | 0.2328 | 0.2328 | 19.0 | 0.0017 | |
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| 1.6769 | 13.0 | 3851 | 1.4820 | 0.2409 | 0.1961 | 0.2333 | 0.2333 | 19.0 | 0.0017 | |
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| 1.659 | 14.0 | 4147 | 1.4740 | 0.2416 | 0.1971 | 0.2341 | 0.2341 | 18.9994 | 0.0017 | |
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| 1.659 | 15.0 | 4443 | 1.4694 | 0.242 | 0.1978 | 0.2345 | 0.2346 | 19.0 | 0.0018 | |
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| 1.6436 | 16.0 | 4740 | 1.4629 | 0.2425 | 0.1981 | 0.2348 | 0.2348 | 19.0 | 0.0018 | |
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| 1.6271 | 17.0 | 5036 | 1.4567 | 0.2428 | 0.1987 | 0.2352 | 0.2353 | 18.9994 | 0.0018 | |
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| 1.6271 | 18.0 | 5332 | 1.4519 | 0.243 | 0.1991 | 0.2355 | 0.2356 | 19.0 | 0.0018 | |
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| 1.6135 | 19.0 | 5628 | 1.4483 | 0.2429 | 0.1992 | 0.2354 | 0.2355 | 18.9994 | 0.0018 | |
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| 1.6135 | 20.0 | 5925 | 1.4425 | 0.2434 | 0.1998 | 0.2359 | 0.236 | 18.9994 | 0.0018 | |
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| 1.6038 | 21.0 | 6221 | 1.4403 | 0.2435 | 0.1999 | 0.2361 | 0.2362 | 18.9994 | 0.0018 | |
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| 1.5958 | 22.0 | 6517 | 1.4370 | 0.2436 | 0.2 | 0.2363 | 0.2364 | 18.9994 | 0.0018 | |
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| 1.5958 | 23.0 | 6813 | 1.4328 | 0.2439 | 0.2003 | 0.2366 | 0.2367 | 18.9994 | 0.0018 | |
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| 1.5875 | 24.0 | 7110 | 1.4308 | 0.2439 | 0.2003 | 0.2366 | 0.2367 | 18.9994 | 0.0018 | |
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| 1.5875 | 25.0 | 7406 | 1.4283 | 0.2439 | 0.2004 | 0.2366 | 0.2367 | 18.9994 | 0.0018 | |
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| 1.581 | 26.0 | 7702 | 1.4255 | 0.2438 | 0.2003 | 0.2365 | 0.2367 | 18.9994 | 0.0018 | |
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| 1.581 | 27.0 | 7998 | 1.4241 | 0.2438 | 0.2005 | 0.2365 | 0.2366 | 18.9994 | 0.0018 | |
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| 1.5734 | 28.0 | 8295 | 1.4212 | 0.244 | 0.2007 | 0.2367 | 0.2368 | 18.9994 | 0.0018 | |
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| 1.5697 | 29.0 | 8591 | 1.4199 | 0.244 | 0.2007 | 0.2367 | 0.2368 | 18.9994 | 0.0018 | |
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| 1.5697 | 30.0 | 8887 | 1.4173 | 0.244 | 0.2007 | 0.2368 | 0.2368 | 18.9994 | 0.0018 | |
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| 1.5639 | 31.0 | 9183 | 1.4168 | 0.2439 | 0.2007 | 0.2367 | 0.2368 | 18.9994 | 0.0018 | |
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| 1.5639 | 32.0 | 9480 | 1.4159 | 0.2441 | 0.2007 | 0.2367 | 0.2368 | 18.9994 | 0.0018 | |
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| 1.5608 | 33.0 | 9776 | 1.4143 | 0.2442 | 0.2009 | 0.2369 | 0.237 | 18.9994 | 0.0018 | |
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| 1.5562 | 34.0 | 10072 | 1.4132 | 0.2442 | 0.2009 | 0.2369 | 0.237 | 18.9994 | 0.0018 | |
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| 1.5562 | 35.0 | 10368 | 1.4123 | 0.2442 | 0.201 | 0.2369 | 0.237 | 18.9994 | 0.0018 | |
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| 1.5563 | 36.0 | 10665 | 1.4122 | 0.2443 | 0.2012 | 0.237 | 0.2371 | 18.9994 | 0.0018 | |
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| 1.5563 | 37.0 | 10961 | 1.4112 | 0.2443 | 0.2011 | 0.237 | 0.2371 | 18.9994 | 0.0018 | |
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| 1.5526 | 38.0 | 11257 | 1.4112 | 0.2444 | 0.2013 | 0.2371 | 0.2373 | 18.9994 | 0.0018 | |
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| 1.5525 | 39.0 | 11553 | 1.4110 | 0.2443 | 0.2012 | 0.237 | 0.2372 | 18.9994 | 0.0018 | |
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| 1.5525 | 39.97 | 11840 | 1.4109 | 0.2444 | 0.2013 | 0.2371 | 0.2372 | 18.9994 | 0.0018 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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