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README.md
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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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- scitldr
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metrics:
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- rouge
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model-index:
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- name: paper-summary
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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: scitldr
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type: scitldr
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config: Abstract
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split: train
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args: Abstract
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metrics:
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- name: Rouge1
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type: rouge
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value: 0.2967
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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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# paper-summary
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the scitldr dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.1243
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- Rouge1: 0.2967
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- Rouge2: 0.1277
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- Rougel: 0.2457
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- Rougelsum: 0.2602
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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: 5.6e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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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: 8
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
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| 2.9845 | 1.0 | 63 | 3.2358 | 0.288 | 0.1219 | 0.238 | 0.2522 |
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| 2.7469 | 2.0 | 126 | 3.1815 | 0.2922 | 0.1251 | 0.2415 | 0.2562 |
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| 2.7032 | 3.0 | 189 | 3.1564 | 0.2991 | 0.1303 | 0.2474 | 0.2619 |
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| 2.6479 | 4.0 | 252 | 3.1354 | 0.2973 | 0.1282 | 0.2457 | 0.2608 |
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| 2.6303 | 5.0 | 315 | 3.1297 | 0.2964 | 0.1272 | 0.2451 | 0.2598 |
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| 2.6115 | 6.0 | 378 | 3.1243 | 0.2967 | 0.1277 | 0.2457 | 0.2602 |
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| 2.5704 | 7.0 | 441 | 3.1264 | 0.2967 | 0.127 | 0.2453 | 0.26 |
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| 2.6028 | 8.0 | 504 | 3.1252 | 0.2971 | 0.1275 | 0.2456 | 0.2604 |
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### Framework versions
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- Transformers 4.24.0
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- Pytorch 1.12.1+cu113
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- Datasets 2.6.1
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- Tokenizers 0.13.1
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