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README.md
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---
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tags:
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- generated_from_trainer
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datasets:
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- big_patent
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metrics:
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- rouge
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model-index:
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- name: nd_pegasus_bigpatent_cnn_xsum_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: big_patent
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type: big_patent
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config: d
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split: train[:200]
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args: d
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metrics:
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- name: Rouge1
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type: rouge
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value: 0.3465
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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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# nd_pegasus_bigpatent_cnn_xsum_model
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This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the big_patent dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.1037
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- Rouge1: 0.3465
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- Rouge2: 0.1181
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- Rougel: 0.2258
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- Rougelsum: 0.227
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- Gen Len: 85.75
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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: 2
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- eval_batch_size: 2
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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: 2
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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 | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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| 3.5734 | 1.0 | 80 | 3.1804 | 0.3468 | 0.1231 | 0.2262 | 0.2268 | 89.95 |
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| 3.3146 | 2.0 | 160 | 3.1037 | 0.3465 | 0.1181 | 0.2258 | 0.227 | 85.75 |
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### Framework versions
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- Transformers 4.27.4
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- Pytorch 1.13.1+cu116
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- Datasets 2.11.0
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- Tokenizers 0.13.2
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