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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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- billsum |
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metrics: |
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- rouge |
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- bleu |
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model-index: |
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- name: flan-t5-base-billsum_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: billsum |
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type: billsum |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.2154 |
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- name: Bleu |
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type: bleu |
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value: 0.0011 |
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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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# flan-t5-base-billsum_model |
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the billsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Rouge1: 0.2154 |
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- Rouge2: 0.1259 |
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- Rougel: 0.1843 |
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- Rougelsum: 0.1843 |
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- Gen Len: 17.3735 |
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- Bleu: 0.0011 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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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: 40 |
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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 | Bleu | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:| |
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| No log | 1.0 | 296 | nan | 0.2154 | 0.1259 | 0.1843 | 0.1843 | 17.3735 | 0.0011 | |
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| 0.0 | 2.0 | 592 | nan | 0.2154 | 0.1259 | 0.1843 | 0.1843 | 17.3735 | 0.0011 | |
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| 0.0 | 3.0 | 888 | nan | 0.2154 | 0.1259 | 0.1843 | 0.1843 | 17.3735 | 0.0011 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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