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lesson-summarization

Text2Text Generation
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t5
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+ ---
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+ license: apache-2.0
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+ base_model: t5-small
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: lesson-summarization
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+ results: []
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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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+ # lesson-summarization
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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 None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.5713
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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: 1
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+ - eval_batch_size: 1
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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: 200
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:-----:|:---------------:|
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+ | 2.9037 | 3.12 | 200 | 2.2456 |
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+ | 2.5914 | 6.25 | 400 | 2.1498 |
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+ | 2.393 | 9.38 | 600 | 2.1002 |
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+ | 2.2409 | 12.5 | 800 | 2.0754 |
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+ | 2.1515 | 15.62 | 1000 | 2.0683 |
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+ | 2.0633 | 18.75 | 1200 | 2.0541 |
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+ | 1.9418 | 21.88 | 1400 | 2.0603 |
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+ | 1.837 | 25.0 | 1600 | 2.0788 |
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+ | 1.7715 | 28.12 | 1800 | 2.0754 |
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+ | 1.6957 | 31.25 | 2000 | 2.0815 |
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+ | 1.6079 | 34.38 | 2200 | 2.0940 |
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+ | 1.5947 | 37.5 | 2400 | 2.1094 |
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+ | 1.4603 | 40.62 | 2600 | 2.1147 |
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+ | 1.4621 | 43.75 | 2800 | 2.1354 |
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+ | 1.4021 | 46.88 | 3000 | 2.1519 |
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+ | 1.3394 | 50.0 | 3200 | 2.1670 |
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+ | 1.2866 | 53.12 | 3400 | 2.1921 |
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+ | 1.2681 | 56.25 | 3600 | 2.2045 |
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+ | 1.1866 | 59.38 | 3800 | 2.2194 |
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+ | 1.2098 | 62.5 | 4000 | 2.2302 |
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+ | 1.1386 | 65.62 | 4200 | 2.2400 |
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+ | 1.0853 | 68.75 | 4400 | 2.2634 |
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+ | 1.0888 | 71.88 | 4600 | 2.2810 |
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+ | 1.0408 | 75.0 | 4800 | 2.2909 |
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+ | 1.0309 | 78.12 | 5000 | 2.3059 |
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+ | 0.9523 | 81.25 | 5200 | 2.3249 |
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+ | 0.9671 | 84.38 | 5400 | 2.3333 |
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+ | 0.9413 | 87.5 | 5600 | 2.3543 |
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+ | 0.9127 | 90.62 | 5800 | 2.3636 |
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+ | 0.9095 | 93.75 | 6000 | 2.3676 |
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+ | 0.8952 | 96.88 | 6200 | 2.3756 |
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+ | 0.857 | 100.0 | 6400 | 2.3878 |
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+ | 0.8474 | 103.12 | 6600 | 2.4148 |
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+ | 0.8215 | 106.25 | 6800 | 2.4231 |
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+ | 0.8172 | 109.38 | 7000 | 2.4243 |
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+ | 0.7761 | 112.5 | 7200 | 2.4489 |
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+ | 0.7737 | 115.62 | 7400 | 2.4718 |
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+ | 0.7476 | 118.75 | 7600 | 2.4614 |
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+ | 0.7345 | 121.88 | 7800 | 2.4705 |
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+ | 0.7426 | 125.0 | 8000 | 2.4740 |
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+ | 0.7151 | 128.12 | 8200 | 2.4833 |
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+ | 0.7191 | 131.25 | 8400 | 2.4786 |
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+ | 0.6818 | 134.38 | 8600 | 2.4882 |
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+ | 0.6862 | 137.5 | 8800 | 2.4938 |
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+ | 0.6929 | 140.62 | 9000 | 2.4977 |
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+ | 0.6494 | 143.75 | 9200 | 2.5195 |
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+ | 0.6689 | 146.88 | 9400 | 2.5185 |
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+ | 0.6492 | 150.0 | 9600 | 2.5259 |
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+ | 0.6384 | 153.12 | 9800 | 2.5259 |
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+ | 0.6435 | 156.25 | 10000 | 2.5287 |
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+ | 0.6251 | 159.38 | 10200 | 2.5284 |
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+ | 0.6295 | 162.5 | 10400 | 2.5398 |
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+ | 0.6324 | 165.62 | 10600 | 2.5442 |
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+ | 0.6252 | 168.75 | 10800 | 2.5481 |
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+ | 0.6108 | 171.88 | 11000 | 2.5455 |
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+ | 0.6034 | 175.0 | 11200 | 2.5502 |
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+ | 0.5969 | 178.12 | 11400 | 2.5601 |
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+ | 0.5949 | 181.25 | 11600 | 2.5617 |
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+ | 0.6183 | 184.38 | 11800 | 2.5679 |
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+ | 0.5805 | 187.5 | 12000 | 2.5687 |
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+ | 0.6032 | 190.62 | 12200 | 2.5708 |
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+ | 0.5955 | 193.75 | 12400 | 2.5709 |
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+ | 0.5961 | 196.88 | 12600 | 2.5713 |
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+ | 0.5914 | 200.0 | 12800 | 2.5713 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 1.13.1
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.3