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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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<!-- 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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# lesson-summarization |
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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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## 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: 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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### Training results |
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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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### Framework versions |
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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 |
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