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End of training

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: sshleifer/distilbart-xsum-12-6
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: bart-abs-1509-0313-lr-0.0003-bs-4-maxep-6
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+ results: []
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+ ---
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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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+
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+ # bart-abs-1509-0313-lr-0.0003-bs-4-maxep-6
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+
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+ This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.8541
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+ - Rouge/rouge1: 0.4315
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+ - Rouge/rouge2: 0.1861
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+ - Rouge/rougel: 0.3638
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+ - Rouge/rougelsum: 0.3654
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+ - Bertscore/bertscore-precision: 0.8936
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+ - Bertscore/bertscore-recall: 0.8875
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+ - Bertscore/bertscore-f1: 0.8904
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+ - Meteor: 0.3814
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+ - Gen Len: 35.4273
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0003
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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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+ - 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: 6
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len |
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+ |:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
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+ | 2.1071 | 1.0 | 217 | 2.5496 | 0.4238 | 0.1753 | 0.3521 | 0.3541 | 0.8943 | 0.8836 | 0.8888 | 0.3359 | 32.1 |
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+ | 1.5392 | 2.0 | 434 | 2.5867 | 0.4296 | 0.1807 | 0.3556 | 0.3551 | 0.8921 | 0.886 | 0.8889 | 0.3598 | 35.0909 |
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+ | 1.0328 | 3.0 | 651 | 2.6952 | 0.4096 | 0.1667 | 0.3444 | 0.3453 | 0.8919 | 0.8826 | 0.8871 | 0.3519 | 33.8818 |
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+ | 0.62 | 4.0 | 868 | 2.9126 | 0.4104 | 0.16 | 0.3478 | 0.3487 | 0.8904 | 0.8815 | 0.8858 | 0.3524 | 33.4273 |
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+ | 0.3251 | 5.0 | 1085 | 3.3250 | 0.43 | 0.1771 | 0.3591 | 0.3598 | 0.8935 | 0.8861 | 0.8896 | 0.3744 | 34.9636 |
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+ | 0.1503 | 6.0 | 1302 | 3.8541 | 0.4315 | 0.1861 | 0.3638 | 0.3654 | 0.8936 | 0.8875 | 0.8904 | 0.3814 | 35.4273 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.0
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+ - Pytorch 2.4.0
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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