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--- |
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license: mit |
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base_model: alexdg19/bert_large_xsum_samsum2 |
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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: bert_large_cnn_daily |
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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: 0.4251 |
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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_cnn_daily |
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This model is a fine-tuned version of [alexdg19/bert_large_xsum_samsum2](https://huggingface.co/alexdg19/bert_large_xsum_samsum2) on the cnn_dailymail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7065 |
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- Rouge1: 0.4251 |
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- Rouge2: 0.2024 |
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- Rougel: 0.2992 |
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- Rougelsum: 0.3961 |
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- Gen Len: 60.6232 |
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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: 3 |
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- eval_batch_size: 3 |
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- seed: 42 |
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- gradient_accumulation_steps: 3 |
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- total_train_batch_size: 9 |
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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: 4 |
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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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| 1.6632 | 1.0 | 1021 | 1.6262 | 0.4191 | 0.1992 | 0.2957 | 0.39 | 60.6205 | |
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| 1.3734 | 2.0 | 2042 | 1.6078 | 0.4253 | 0.2046 | 0.3009 | 0.397 | 61.0692 | |
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| 1.1497 | 3.0 | 3064 | 1.6759 | 0.4254 | 0.2033 | 0.2998 | 0.3967 | 60.8555 | |
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| 1.0123 | 4.0 | 4084 | 1.7065 | 0.4251 | 0.2024 | 0.2992 | 0.3961 | 60.6232 | |
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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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