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--- |
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license: mit |
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base_model: facebook/bart-large-xsum |
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tags: |
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- generated_from_trainer |
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datasets: |
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- samsum |
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metrics: |
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- rouge |
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model-index: |
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- name: bert_large_xsum_samsum |
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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: samsum |
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type: samsum |
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config: samsum |
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split: test |
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args: samsum |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.5083 |
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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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# bert_large_xsum_samsum |
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This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co./facebook/bart-large-xsum) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9030 |
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- Rouge1: 0.5083 |
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- Rouge2: 0.2528 |
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- Rougel: 0.41 |
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- Rougelsum: 0.4105 |
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- Gen Len: 29.0183 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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: 5 |
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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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| No log | 1.0 | 41 | 1.6008 | 0.4779 | 0.2349 | 0.4058 | 0.4056 | 21.1037 | |
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| No log | 2.0 | 82 | 1.5804 | 0.5104 | 0.2526 | 0.4242 | 0.4239 | 24.689 | |
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| No log | 3.0 | 123 | 1.7310 | 0.5148 | 0.253 | 0.4162 | 0.4155 | 28.0793 | |
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| No log | 4.0 | 164 | 1.7974 | 0.5019 | 0.2443 | 0.4127 | 0.4125 | 25.189 | |
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| No log | 5.0 | 205 | 1.9030 | 0.5083 | 0.2528 | 0.41 | 0.4105 | 29.0183 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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