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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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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: LLM_Teached_Bart_From_Scratch |
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results: [] |
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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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# LLM_Teached_Bart_From_Scratch |
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This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4999 |
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- Rouge1: 0.4331 |
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- Rouge2: 0.2164 |
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- Rougel: 0.3724 |
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- Rougelsum: 0.3725 |
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- Gen Len: 19.9255 |
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- Precision: 0.9125 |
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- Recall: 0.8885 |
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- F1: 0.9002 |
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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: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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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| No log | 1.0 | 390 | 1.5709 | 0.4119 | 0.2002 | 0.3529 | 0.3527 | 19.9709 | 0.9093 | 0.8846 | 0.8966 | |
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| 1.8155 | 2.0 | 781 | 1.5361 | 0.4331 | 0.2157 | 0.3717 | 0.3717 | 19.9185 | 0.9123 | 0.8889 | 0.9003 | |
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| 1.5875 | 3.0 | 1172 | 1.5030 | 0.4263 | 0.2129 | 0.3671 | 0.3673 | 19.9545 | 0.9117 | 0.8871 | 0.899 | |
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| 1.4978 | 3.99 | 1560 | 1.4999 | 0.4331 | 0.2164 | 0.3724 | 0.3725 | 19.9255 | 0.9125 | 0.8885 | 0.9002 | |
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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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