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--- |
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license: apache-2.0 |
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base_model: buianh0803/Text_Summarization |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cnn_dailymail |
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metrics: |
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- rouge |
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model-index: |
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- name: text-sum |
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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: cnn_dailymail |
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type: cnn_dailymail |
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config: 3.0.0 |
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split: test |
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args: 3.0.0 |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.2484 |
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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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# text-sum |
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This model is a fine-tuned version of [buianh0803/Text_Summarization](https://huggingface.co/buianh0803/Text_Summarization) on the cnn_dailymail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6668 |
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- Rouge1: 0.2484 |
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- Rouge2: 0.1187 |
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- Rougel: 0.2056 |
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- Rougelsum: 0.2055 |
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- Gen Len: 18.9986 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 5 |
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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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| 1.8345 | 1.0 | 17945 | 1.6835 | 0.2475 | 0.118 | 0.2047 | 0.2047 | 18.998 | |
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| 1.8152 | 2.0 | 35890 | 1.6720 | 0.2479 | 0.1179 | 0.2048 | 0.2048 | 18.9986 | |
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| 1.7954 | 3.0 | 53835 | 1.6712 | 0.2477 | 0.1182 | 0.205 | 0.2051 | 18.9981 | |
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| 1.7975 | 4.0 | 71780 | 1.6680 | 0.2482 | 0.1186 | 0.2054 | 0.2054 | 18.9981 | |
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| 1.7924 | 5.0 | 89725 | 1.6668 | 0.2484 | 0.1187 | 0.2056 | 0.2055 | 18.9986 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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