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alaahussein
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t5-small-billsum_model

Text2Text Generation
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t5
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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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+
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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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+
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+ # t5-small-billsum_model
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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