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--- |
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license: apache-2.0 |
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base_model: facebook/bart-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- reddit_tifu |
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metrics: |
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- rouge |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: Bart_reddit_tifu |
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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: reddit_tifu |
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type: reddit_tifu |
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config: long |
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split: train |
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args: long |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.2709 |
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- name: Precision |
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type: precision |
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value: 0.8768 |
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- name: Recall |
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type: recall |
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value: 0.8648 |
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- name: F1 |
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type: f1 |
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value: 0.8705 |
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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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# Bart_reddit_tifu |
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This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the reddit_tifu dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5035 |
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- Rouge1: 0.2709 |
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- Rouge2: 0.0948 |
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- Rougel: 0.2244 |
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- Rougelsum: 0.2244 |
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- Gen Len: 19.3555 |
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- Precision: 0.8768 |
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- Recall: 0.8648 |
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- F1: 0.8705 |
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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: 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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:| |
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| 2.6968 | 1.0 | 2370 | 2.5385 | 0.2634 | 0.0907 | 0.218 | 0.2182 | 19.4438 | 0.8766 | 0.8641 | 0.8701 | |
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| 2.4746 | 2.0 | 4741 | 2.5077 | 0.273 | 0.0941 | 0.2238 | 0.2239 | 19.2572 | 0.8774 | 0.8655 | 0.8712 | |
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| 2.3066 | 3.0 | 7111 | 2.5012 | 0.2671 | 0.0936 | 0.221 | 0.2211 | 19.3071 | 0.8756 | 0.864 | 0.8696 | |
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| 2.2041 | 4.0 | 9480 | 2.5035 | 0.2709 | 0.0948 | 0.2244 | 0.2244 | 19.3555 | 0.8768 | 0.8648 | 0.8705 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.15.0 |
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