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t5-small-finetuned-cnndm_3epoch_v2

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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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+ - cnn_dailymail
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+ metrics:
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+ - rouge
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+ model-index:
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+ - name: t5-small-finetuned-cnndm_3epoch_v2
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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: cnn_dailymail
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+ type: cnn_dailymail
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+ args: 3.0.0
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+ metrics:
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+ - name: Rouge1
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+ type: rouge
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+ value: 24.7696
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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-finetuned-cnndm_3epoch_v2
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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 cnn_dailymail dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.6070
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+ - Rouge1: 24.7696
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+ - Rouge2: 11.9467
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+ - Rougel: 20.4495
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+ - Rougelsum: 23.3341
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+ - Gen Len: 18.9999
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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: 0.0003
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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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+ - 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: 3
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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 |
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+ |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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+ | 1.9695 | 0.07 | 5000 | 1.7781 | 24.2253 | 11.472 | 20.0367 | 22.8469 | 18.9962 |
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+ | 1.9536 | 0.14 | 10000 | 1.7575 | 24.2983 | 11.469 | 20.0054 | 22.9144 | 18.9995 |
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+ | 1.9452 | 0.21 | 15000 | 1.7406 | 24.2068 | 11.4601 | 20.0021 | 22.8375 | 19.0 |
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+ | 1.931 | 0.28 | 20000 | 1.7302 | 24.1589 | 11.4183 | 19.9736 | 22.7804 | 18.9996 |
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+ | 1.9182 | 0.35 | 25000 | 1.7381 | 24.1634 | 11.5435 | 19.9643 | 22.7371 | 18.9999 |
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+ | 1.9072 | 0.42 | 30000 | 1.7239 | 24.4401 | 11.6323 | 20.1243 | 22.9468 | 19.0 |
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+ | 1.9027 | 0.49 | 35000 | 1.7162 | 24.1801 | 11.4788 | 20.0011 | 22.832 | 18.9996 |
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+ | 1.8962 | 0.56 | 40000 | 1.7060 | 24.4153 | 11.6275 | 20.1742 | 23.0865 | 18.9998 |
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+ | 1.8905 | 0.63 | 45000 | 1.7004 | 24.1446 | 11.5402 | 19.9986 | 22.7949 | 18.9983 |
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+ | 1.8764 | 0.7 | 50000 | 1.6876 | 24.342 | 11.5448 | 20.0993 | 22.9509 | 18.9993 |
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+ | 1.8772 | 0.77 | 55000 | 1.6879 | 24.3596 | 11.6063 | 20.1592 | 22.9966 | 19.0 |
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+ | 1.8669 | 0.84 | 60000 | 1.6776 | 24.6201 | 11.6668 | 20.2639 | 23.201 | 18.9994 |
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+ | 1.8692 | 0.91 | 65000 | 1.6838 | 24.2924 | 11.6129 | 20.1071 | 22.9112 | 18.9997 |
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+ | 1.8552 | 0.98 | 70000 | 1.6885 | 24.2878 | 11.6773 | 20.1272 | 22.8797 | 18.9992 |
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+ | 1.8205 | 1.04 | 75000 | 1.6717 | 24.5579 | 11.6421 | 20.2593 | 23.1442 | 19.0 |
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+ | 1.8074 | 1.11 | 80000 | 1.6604 | 24.495 | 11.6542 | 20.1854 | 23.1091 | 18.9996 |
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+ | 1.7951 | 1.18 | 85000 | 1.6705 | 24.4504 | 11.6601 | 20.2185 | 23.0597 | 18.9999 |
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+ | 1.7937 | 1.25 | 90000 | 1.6645 | 24.5535 | 11.6921 | 20.2087 | 23.1099 | 18.9999 |
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+ | 1.8017 | 1.32 | 95000 | 1.6647 | 24.5179 | 11.8005 | 20.2903 | 23.13 | 18.9993 |
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+ | 1.7918 | 1.39 | 100000 | 1.6568 | 24.518 | 11.7528 | 20.222 | 23.0767 | 18.9991 |
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+ | 1.7985 | 1.46 | 105000 | 1.6588 | 24.4636 | 11.636 | 20.1038 | 23.032 | 19.0 |
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+ | 1.7944 | 1.53 | 110000 | 1.6498 | 24.6611 | 11.78 | 20.3059 | 23.2404 | 18.9999 |
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+ | 1.7934 | 1.6 | 115000 | 1.6551 | 24.7267 | 11.823 | 20.3377 | 23.273 | 18.9997 |
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+ | 1.7764 | 1.67 | 120000 | 1.6467 | 24.5052 | 11.8052 | 20.2617 | 23.1228 | 18.9996 |
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+ | 1.7846 | 1.74 | 125000 | 1.6489 | 24.5423 | 11.8407 | 20.3464 | 23.1433 | 18.9999 |
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+ | 1.7799 | 1.81 | 130000 | 1.6438 | 24.4915 | 11.7827 | 20.2592 | 23.1299 | 18.9999 |
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+ | 1.7806 | 1.88 | 135000 | 1.6353 | 24.7804 | 11.9212 | 20.4678 | 23.359 | 19.0 |
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+ | 1.7784 | 1.95 | 140000 | 1.6338 | 24.7892 | 11.8836 | 20.4227 | 23.373 | 18.9997 |
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+ | 1.7551 | 2.02 | 145000 | 1.6341 | 24.6828 | 11.8257 | 20.3862 | 23.2536 | 18.9997 |
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+ | 1.728 | 2.09 | 150000 | 1.6328 | 24.6697 | 11.851 | 20.3943 | 23.2738 | 18.9993 |
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+ | 1.7201 | 2.16 | 155000 | 1.6309 | 24.7364 | 11.8505 | 20.365 | 23.2885 | 18.9992 |
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+ | 1.7233 | 2.23 | 160000 | 1.6346 | 24.7298 | 12.0026 | 20.4444 | 23.3156 | 18.9999 |
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+ | 1.7096 | 2.3 | 165000 | 1.6253 | 24.6443 | 11.9004 | 20.4138 | 23.2583 | 18.9999 |
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+ | 1.7084 | 2.37 | 170000 | 1.6233 | 24.6688 | 11.8885 | 20.3623 | 23.2608 | 18.9996 |
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+ | 1.7236 | 2.44 | 175000 | 1.6243 | 24.7174 | 11.8924 | 20.4012 | 23.2948 | 18.9996 |
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+ | 1.7108 | 2.51 | 180000 | 1.6188 | 24.6013 | 11.8153 | 20.2969 | 23.1867 | 18.9997 |
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+ | 1.711 | 2.58 | 185000 | 1.6125 | 24.7673 | 11.8646 | 20.3805 | 23.3114 | 18.9997 |
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+ | 1.7108 | 2.65 | 190000 | 1.6101 | 24.8047 | 11.9763 | 20.494 | 23.3873 | 18.9998 |
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+ | 1.7114 | 2.72 | 195000 | 1.6123 | 24.7019 | 11.9201 | 20.414 | 23.2823 | 18.9999 |
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+ | 1.7004 | 2.79 | 200000 | 1.6083 | 24.7525 | 11.9197 | 20.4581 | 23.3371 | 18.9999 |
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+ | 1.7104 | 2.86 | 205000 | 1.6061 | 24.7057 | 11.8818 | 20.4017 | 23.286 | 18.9999 |
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+ | 1.7063 | 2.93 | 210000 | 1.6063 | 24.7707 | 11.934 | 20.4473 | 23.3316 | 18.9999 |
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+ | 1.7039 | 3.0 | 215000 | 1.6070 | 24.7696 | 11.9467 | 20.4495 | 23.3341 | 18.9999 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.17.0
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+ - Pytorch 1.10.0+cu111
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+ - Datasets 2.0.0
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+ - Tokenizers 0.11.6