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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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- samsum |
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metrics: |
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- rouge |
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model-index: |
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- name: flan_t5_small_finetuned |
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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: train[:50] |
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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: 44.9038 |
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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_small_finetuned |
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3412 |
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- Rouge1: 44.9038 |
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- Rouge2: 22.7667 |
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- Rougel: 38.8789 |
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- Rougelsum: 41.7196 |
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- Gen Len: 16.92 |
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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: 5e-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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- 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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| No log | 1.0 | 7 | 1.4537 | 43.8958 | 21.4426 | 37.7876 | 41.0621 | 17.4 | |
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| No log | 2.0 | 14 | 1.4025 | 44.291 | 21.7752 | 37.6394 | 41.0092 | 17.1 | |
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| No log | 3.0 | 21 | 1.3687 | 44.5803 | 22.5489 | 38.5959 | 41.6202 | 17.0 | |
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| No log | 4.0 | 28 | 1.3487 | 44.6139 | 22.5884 | 38.6786 | 41.5311 | 16.94 | |
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| No log | 5.0 | 35 | 1.3412 | 44.9038 | 22.7667 | 38.8789 | 41.7196 | 16.92 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.13.1+cpu |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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